Constituent: Definition and Examples in Grammar

Getting to the Root of a Sentence or Phrase

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  • An Introduction to Punctuation
  • Ph.D., Rhetoric and English, University of Georgia
  • M.A., Modern English and American Literature, University of Leicester
  • B.A., English, State University of New York

In English grammar , a constituent is a linguistic part of a larger sentence, phrase, or clause. For instance, all the words and phrases that make up a sentence are said to be  constituents  of that sentence. A constituent can be a  morpheme ,  word ,  phrase , or  clause . Sentence analysis identifies the subject or predicate or different parts of speech, a process known as parsing  the sentence into its constituents. It actually sounds more complicated than it is.

Key Takeaways: Constituents in Grammar

  • Constituents in grammar define the structural pieces of a sentence, phrase, or clause. 
  • Constituents can be phrases, words, or morphemes. 
  • Immediate Constituent Analysis is a way to identify the components.
  • Analysis can be used to identify the structure of a given sentence, discover its deep meaning, and explore alternative ways of expressing the meaning. 

Constituent Definition 

Every sentence (and every phrase and clause) has constituents. That is to say, every sentence is made up of parts of other things that work together to make the sentence meaningful.

For example, in the sentence: "My dog Aristotle bit the postal carrier on the ankle," the constituent parts are the subject, made up of a Noun Phrase ("my dog Aristotle"), and the predicate, a Verb Phrase ("bit the postal carrier on the ankle").

  • A Noun Phrase (abbreviated NP) is made up of a noun and its modifiers. Modifiers that come before the noun include articles, possessive nouns, possessive pronouns, adjectives, or participles. Modifiers that come after include prepositional phrases, adjective clauses, and participle phrases.
  • A Verb Phrase (VP) is made up of a verb and its dependents (objects, complements, and modifiers).

Each of the phrases in the sentence can be further broken down into its own constituents. The Subject NP includes the noun ("Aristotle") and a possessive pronoun and noun ("My dog") that modify Aristotle. The Verb Phrase includes the verb ("bit"), the NP "the postal carrier," and the prepositional phrase "on the ankle."

Immediate Constituent Analysis

One method of analyzing sentences , commonly known as immediate constituent analysis (or IC analysis), was introduced by the American linguist Leonard Bloomfield. As Bloomfield identified it, IC analysis involves breaking a sentence down into its parts and illustrating it with brackets or a tree diagram. Though originally associated with structural linguistics , IC analysis continues to be used (in various forms) by many contemporary grammarians . 

The purpose of Immediate Constituent Analysis is to understand the way sentences are structured, as well as discover the deep meaning of the intended sentence and perhaps how it might be better expressed.

In this diagram, the sentence "My dog Aristotle bit the postal carrier on the ankle" has been broken down (or "parsed") into its separate constituents. The sentence contains a subject and predicate , parsed as Noun Phrase and Verb Phrase : those two things are known as the Immediate Constituents of the sentence. Each IC is then further analyzed into its own constituent parts—the IC of the Verb Phrase includes another Verb Phrase ("bit the postal carrier") and a Prepositional Phrase ("on the ankle"). The contents of the IC—for example, the subject noun phrase includes determiner, noun, and modifier—are known as the ultimate constituents (UC) of that construction; they cannot be further broken down.

The sentence "The boy will sing," contains four word forms: an article (the), a noun (boy), a modal verb (will), and a verb (sing). Constituent analysis recognizes only two parts: the subject or noun phrase (the boy) and the predicate or verb phrase "will sing."

The Substitution Test

So far, the sentences have been fairly straightforward. In the sentence "Edward grows tomatoes as large as grapefruit," the constituent parts are the subject (that would be Edward) and the predicate ("grows tomatoes"); another constituent is the phrase "as large as grapefruit," a noun phrase that modifies the noun of the predicate. In constituent analysis, you're looking for the basic underlying structure.

The substitution test, or more properly "proform substitution," helps identify the underlying structure by replacing a text string in a sentence with a suitable definite pronoun. That allows you to determine whether the sentence constituents are broken down into the smallest salient pieces, words that can be replaced by a single part of speech. The sentence "My dog Aristotle bit the postal carrier on the ankle" could be reduced to "He bit (something)" and "something" is the object of the verb, so there are two main parts—noun and verb—and each of those is considered a constituent part of the sentence in the diagram.

To get to the bottom of Edward and his tomatoes, textbook authors Klammer, Schulz, and Volpe walk us through the logic by using the substitution test:

"​ Edward , the subject, is a single noun and is, according to our definition, a noun phrase as well. The main verb grows stands alone without any auxiliaries and is the entire main verb phrase. Although tomatoes , by itself, could be a noun phrase, in identifying constituents of the sentence, we are looking for the largest sequence of words that can be replaced by a single part of speech : a noun, a verb, an adjective, or an adverb. Two facts suggest that tomatoes as large as grapefruit be considered as a single unit. First, in this sentence, the entire phrase can be replaced either by a single word tomatoes (or by a pronoun like something ), yielding a complete sentence: Edward grows tomatoes or Edward grows something. Second, if you divide this structure, no single word can replace as large as grapefruit in this structure, while supplying similar information about the tomatoes. If, for example, you try to substitute a simple adjective like big for the phrase, you get * Edward grows tomatoes big . Thus, the complete sequence tomatoes as large as grapefruit is a noun phrase constituting part of the predicate, and we identify the sentence constituents as follows:
A noun phrase subject: Edward
A verb phrase predicate: grows tomatoes as large as grapefruit
A main verb phrase: grows
A second noun phrase: tomatoes as large as grapefruit."
  • Bloomfield, Leonard. "Language," 2nd ed. Chicago: University of Chicago Press, 1984. 
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  • Adjuncts in English Grammar
  • Predicators or Main Verbs in English Grammar
  • What Is a Phrase? Definition and Examples in Grammar
  • 100 Key Terms Used in the Study of Grammar
  • Relative Adverbs in English
  • Definition and Examples of a Predicate
  • Nominal: Definition and Examples in Grammar
  • Prepositional Phrases in English Grammar
  • Subject in English Grammar
  • Complement Clause in Grammar
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The U.S. Intelligence Community and Foreign Policy: Getting Analysis Right

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Kenneth g. lieberthal kenneth g. lieberthal senior fellow emeritus - foreign policy.

September 15, 2009

Executive Summary

Intelligence analysis seeks to provide necessary information in a timely manner to help policymakers from the president on down make better decisions. The information and judgments must be pertinent to what policymakers need to know but not skewed to support a particular policy outcome. In reality, this is more of an art than a science, especially because the manner and means of most effectively informing the president and other senior policymakers changes with the preferences and working style of each new administration.

The Intelligence Community (IC) of the United States has been undergoing major reforms since 2005 when President George W. Bush signed the Intelligence Reform and Terrorism Prevention Act. Under the new Director of National Intelligence, the shortcomings in intelligence analysis that came to light in the wake of the 9/11 and Iraq WMD intelligence failures are being addressed through revamped analytic standards, increased resources for the IC, and numerous organizational and procedural changes. These analytic transformation initiatives seek to reduce barriers among organizations and individuals across the IC and to more effectively prioritize missions.

As of now, many of these innovative initiatives are in the development stage. Once completed, given their conceptual and technological complexity, it will be important to continually assess whether these initiatives result in a significantly improved analytic product. Mindsets and cultures of various IC components may prove serious obstacles to the kind of open and collaborative environment envisioned in these efforts; these new capabilities may prove most effective among digitally- savvy analysts in their twenties rather than among more senior analysts and managers.

Against this background of ongoing reform, this study assesses the current state of play, identifies systemic concerns, and offers practical ideas to improve analytic transformation and make the interactions between the analytic community and policymakers more effective. Extensive interviews with current and former policymakers and intelligence community analysts and managers reveal that there are flaws in the current system that require dedicated attention. The most consequential include:

  • Overemphasis on the President’s Daily Brief (PDB) – President George W. Bush elevated the PDB to an unprecedented level of importance, which had the unintended effect of skewing intelligence production away from deeper research and arms-length analysis to being driven by the latest, attention-grabbing clandestine reports from the field.
  • Disappointing National Intelligence Estimates (NIEs) – NIEs are meant to be one of the major products of the IC, yet they are frequently too late, too long, and too detailed to serve high-level policymakers well. Moreover, NIE analytic quality is often compromised by the effort to present a unified analytic position, producing reports that can become the lowest common denominator statement that is able to achieve agreement across the IC silos.
  • Analytic Risk Aversion – In the wake of the Iraq WMD fiasco, the pendulum has swung decidedly toward a tendency for analytical products to focus on amalgamating all potentially relevant data and to present only that to policymakers—leaving it up to them to draw the analytic conclusions. DNI Dennis Blair has recently made a welcome commitment to having opportunity analysis— the identification by analysts of unanticipated windows of opportunity to advance U.S. policies—become a key component of intelligence products.
  • Insufficiently Deep Country Knowledge – Many of the young IC analysts are trained to follow a particular stream of information from ”their” country but lack the deep immersion in the country’s political system, economy, and modern history necessary to produce nuanced, insightful analytic products. Moreover, very burdensome security constraints make it extremely difficult for them to build that kind of analytic depth.
  • Overemphasis on Classified Sources – IC analysts tend to gravitate to information obtained by clandestine means. Yet much of that information lacks context and is substantively rather marginal. As a consequence, analyses overly driven by classified sources may suffer from ignorance of important information in unclassified sources. This is especially notable with the explosion of unclassified material now available on key targets such as China.

This report’s recommendations to address these shortcomings fall into three broad categories.

On improving the capabilities of analysts:

  • Recruit a greater percentage of the incoming class of analysts from those in their late twenties and early thirties who have had extensive experience related to the country of concern – This change can present a security challenge but the added benefits in terms of maturity, life experience, and deeper country knowledge are worth the additional effort and attention needed to clear these individuals.
  • Establish a National Intelligence University with its own campus and faculty – If the vision of a truly integrated analytic corps is to be achieved, there needs to be an academy that allows the IC to not only establish crossagency relationships and cultivate common standards and procedures, but also to better draw lessons from its own historical successes and failures and to incorporate those into training programs.
  • Devote greater time and attention to formal training – To address the question of analytic depth, special short-term courses that draw in specialists from outside of the IC and that test participants’ learning in the course should be conducted on a regular basis. Moreover, analysts should be encouraged to attend programs held by various Washington-area think tanks, not (as is now the case for many) discouraged due to security concerns.
  • Nurture and reward area specialists – There is no substitute for the key analyst with deep substantive knowledge and experience on a single country or issue. The IC may wish to consider assigning some analysts to conduct in-depth studies of major long-term issues in key countries such as China (e.g. study of the long-term evolution if civilian-military relations in the PRC) in order to help a cohort of analysts develop such depth.
  • Break stovepipes in analytic assignments – On National Intelligence Estimates and other key products, consideration should be given more often to assigning individuals from two different disciplines joint leadership in developing the analysis. This would foster, for example, greater integration of political and technical analysis of missile development.

On improving the utility of IC analytical products for policymakers:

  • Provide formal introductory briefings for incoming policymakers on IC capabilities and limitations – Often, new policymakers come into office with very impressionistic and misinformed views on what the IC is able to produce. Senior IC managers should develop introductory briefings that help policymakers think critically about their intelligence needs and how they can best utilize the IC.
  • Assign IC analysts systematically to provide on-site support to policymakers at and above the assistant secretary level – This not only can help the policy maker but also can provide invaluable feedback to the IC about the policy maker’s actual intelligence needs.
  • Develop regular feedback mechanisms from the policymaker to analysts – Periodic meetings can greatly help the IC understand the look-ahead intelligence requirements of policymakers and garner critical feedback on materials sent over since the last such meeting.
  • Allow for NIEs with formal dissenting opinions, similar to Supreme Court decisions – In such NIEs, dissenters can write specific dissenting opinions and even those who agree can pen concurring opinions that articulate a distinctive analytical approach.
  • Train analysts in the power dynamic between analysts and policymakers – The desire of analysts to please the most senior intelligence consumers who are driving to a decision based, in part, on intelligence judgment can lead analysts unintentionally to overstate their confidence in the intelligence. Analysts need to be better trained and equipped to understand the subtle effects of power dynamics between analysts and policymakers, and policymakers need to keep in mind that their power and positions are intimidating to many analysts who brief them.

On improving the ability of policymakers to elicit and utilize high quality IC analysis:

  • Encourage policymakers to better articulate their intelligence questions and priorities – Taking the time to think through the analytic question they want answered will pay dividends for policymakers. Requests that do not assume the form of analytical questions too often fail to motivate IC analysts to think through the implications of their data, debate the relative significant of different factors, and make explicit their levels of confidence in their responses.
  • Elicit what analysts know, what they don’t know, and what they think is likely to happen – Former Secretary of State Colin Powell told his IC briefers that they would be responsible if he took action based on what they said they know and do not know but that he would be responsible if he took action based on what analysts said when asked what they think is likely to happen. As a result, he incentivized analysts to be both rigorous and thoughtful.
  • Provide the IC with the insights the policymakers themselves gain from their meetings with foreign officials – Presidents and many other senior policymakers are experts at “reading” other political leaders—a skill most IC analysts understandably do not share. If such insights are routinely shared they may improve the quality of intelligence analysis, especially as regards elite politics.
  • Avoid as much as possible the temptation to declassify NIEs – When NIEs are likely to be declassified, analysts are prone—either consciously or subconsciously—to pull their punches and hedge their analysis. Moreover, the impulse to declassify NIEs or to leak selectively from NIEs is often based on the faulty assumption that the IC’s analysis can and should authoritatively settle a policy debate.

In the wake of failures early in this decade, the Intelligence Community today has both the opportunity and obligation to transform itself. With fifty percent of the IC workforce hired since 9/11, there is now a large pool of young, technologysavvy talent that is eager to be shaped into a superior new IC. Indeed, cultural shifts based on the information age almost guarantee that many important changes will happen simply because of the nature and talents of this younger generation.

Ongoing IC cultures of insularity and secrecy, though, present major obstacles to realizing the IC’s full potential. For example, some IC managers continue to deny information to other parts of the community because they do not utilize identical security screenings, such as the polygraph. To cite another example, the need for a National Intelligence University has been understood for some time, but the IC’s sixteen disparate agencies still resist merging their educational and training programs. This resistance highlights that the IC still has some distance to go in terms of individual agency cultures and mindsets if it is to be truly unified under the leadership of the DNI.

The division of labor and of tasking among the major components of the IC should remain a concern. Post 9/11 changes created the ODNI and repositioned the CIA and the NIC, among other shifts. In short, key pieces have been moved on the IC chessboard, and such major changes inevitably require a substantial period of time to gel fully. This report does not, therefore, provide specific recommendations on additional changes in the distribution of responsibilities and authorities among the major IC players. But the research suggests that a thoughtful review of current relationships—especially those among ODNI, the NIC, CIA, DIA, and INR—might prove of considerable value again in about two to three years.

Finally, the task of analytic transformation cannot fall on the IC alone. Policymakers can affect the quality of analysis if they do take the time to provide clear and candid feedback to the IC. Policymakers also should understand the process of intelligence analysis to the point that they can read products as well-informed customers. It would be helpful to good analysis if policymakers realized their own value as IC sources. They should in particular inform analysts of relevant discussions with foreign leaders that may shed light on intentions and motivations. Too often policymakers simply assume that analysts know what the policymakers themselves know, and that comes at some cost to insightful IC analysis.

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Intelligence Analysis for Tomorrow: Advances from the Behavioral and Social Sciences (2011)

Chapter: 1 challenges for the intelligence community, 1 challenges for the intelligence community.

Measures to improve intelligence analysis have to adapt lessons from the behavioral and social sciences to the unique circumstances of analysts and their national security customers.

The primary missions of the intelligence community (IC) are to reduce uncertainty and provide warning about potential threats to the national security of the United States, the safety of its citizens, and its interests around the world. Decision makers—from the White House and Capitol Hill to battlefields and local jurisdictions around the globe—demand and depend on information and insights from IC analysts. The list of individual and agency customers is long, diverse, and growing. So, too, is the array of issues that analysts are expected to monitor: see Box 1-1 ; also see Office of the Director of National Intelligence (2009a, 2009b, 2009c).

STRUCTURE OF THE INTELLIGENCE COMMUNITY

The IC is a complex enterprise with approximately 100,000 military and civilian U.S. government personnel (Sanders, 2008). Of this number, roughly 20,000 work as analysts, a category that includes both intelligence analysts who work primarily with information obtained from a single type of source, such as imagery, intercepted signals, clandestine human intelligence, diplomatic and attaché reporting, and “open source” or unclassified information and analysts who routinely work with information obtained from many sources ( all-source analysts) (for a review, see Fingar, 2011). The distinction between these two types of analyst was once seen as fundamental. Today, it is widely understood that all analysts must use information and insight from multiple sources. For example, imagery analysts must use signals intelligence (SIGINT) and human intelligence (HUMINT) to clarify what they observe in imagery intelligence (IMINT).

As an integral part of the intelligence collection cycle, analysts both drive collection of and receive huge—and rapidly increasing—amounts of information. The collectors include both technical systems and human intelligence officers who obtain, process, and disseminate “raw” intelligence. The National Security Agency (NSA), for example, intercepts millions of

signals every hour (Bamford, 2002), and the National Counterterrorism Center processes thousands of names of potential terrorists every day (Blair and Leiter, 2010). In recent years, the collection, information processing, storage, and retrieval capabilities of the IC have improved dramatically, but the ultimate value of all this information still depends on the capabilities

of the analysts who receive it. They must consider new information against previous analyses, interpret and evaluate evidence, imagine hypotheses, identify anomalies, and communicate their findings to decision makers in ways that help them to fulfill their missions.

Most of the roughly 20,000 analysts in the IC work for one of 16 offices and agencies scattered across the federal government and overseen by the Director of National Intelligence (DNI). In addition, IC analysts work for three entities—the National Intelligence Council, the National Counter-terrorism Center, and the National Counterintelligence Executive—that are part of the Office of the Director of National Intelligence (ODNI). One of the 16 agencies overseen by the DNI, the Central Intelligence Agency (CIA), is an independent agency. The other 15 entities are parts of different departments, agencies, and military branches: see Figure 1-1 . IC member agencies range in size from the very small (e.g., the analytic component of the Drug Enforcement Administration’s Office of National Security Intelligence) to the very large (e.g., the National Security Agency [NSA], the CIA, and the Federal Bureau of Investigation’s National Security Branch). The expertise required of analysts in each entity depends on their customers’ missions and priorities. For example, the Air Force and the Defense Intelligence Agency require more missile expertise than does the Department of Energy’s Office of Intelligence and Counterintelligence. Similarly, the State Department’s Bureau of Intelligence and Research and the CIA’s analytic component, the Directorate of Intelligence, require more country-specific political expertise than do the military services’ intelligence components.

These entities differ in missions and the desire for analysts trained and directly accountable to meet the agencies’ needs. The (literal and figurative) proximity of analysts and customers improves communication and trust between them, but having so many specialized intelligence units also creates problems. Chief among the problems are bureaucratic divisions that can isolate intelligence in “stovepipes” and lead to inconsistent standards, practices, and even terminology, which complicates interagency cooperation and confuses customers.

Broadly speaking, the nation’s confederated intelligence system has produced specialization at the expense of integration and collaboration. The IC’s inability to function as a unified team has been the subject of more than 40 major studies since the CIA’s establishment in 1947 (Zegart, 2007). The creation of the ODNI, after 9/11, was the latest and most serious effort in a long line of initiatives to transform the IC from a collection of semiautonomous agencies into an integrated intelligence system.

Both the strengths and the weaknesses of today’s IC structure must be recognized when considering ways to improve analysis. For example, efforts to reduce stovepiping should not undermine analysts’ ability to address the specific needs of their customers. The need for tailored intelligence is so

FIGURE 1-1 Members of the U.S. intelligence community.

FIGURE 1-1 Members of the U.S. intelligence community.

SOURCE: Data from Office of the Director of National Intelligence (2009a, 2009b).

strong that no agency has advocated abolishing its dedicated unit, and some agencies that do not have such units continue to want them, despite recognizing the price paid for compartmentalization. The unsuccessful bombing attempt of a Northwest Airlines flight on Christmas Day 2009 showed that the IC is still struggling to solve the collaboration and integration problems (Blair and Leiter, 2010).

MISSION-RELATED CHALLENGES

The challenges facing the IC today are of two types: those specifically related to its mission and those facing virtually all complex organizations. Both types have to be considered when seeking to improve intelligence analysis.

The IC is still adjusting to the dramatic shift from the Cold War era to the very different demands of the 21st century. This shift requires moving from one core “target” (the Soviet Union and its allies) to many diverse targets, from existential threats to national survival to threats to specific U.S. targets, and from demands for general information (e.g., country A is providing certain types of weapons to country B or to insurgent group C)

to demands for “actionable intelligence” relevant to interdicting a specific ship, aircraft, or person. Discovering that the Soviet Union had nuclear weapons aimed at every U.S. city greater than a certain size created very different collection and analytic requirements than those needed to discover that a terrorist group plans to explode an improvised radiological device in a city, shopping mall, or school. The United States did not evacuate its cities in response to the nationwide threat of nuclear annihilation, but officials might choose to evacuate a shopping center if the IC reported a 10 percent chance of a terrorist attack during a specified period. Each of these changes in the world has implications for what IC analysts are expected to know and for how they do their jobs.

The Military and Other Customers

The military has long been the IC’s dominant customer, with intelligence needs for a wide variety of missions and officials, including the Office of the Secretary of Defense, the Joint Chiefs of Staff, commanders of tactical operations, and designers of equipment and tactics. As a result, much of the IC has evolved to meet military requirements. This focus has, among other things, created a predisposition for the worst-case analyses needed by those designing equipment or preparing for battle (Powell, 2004). It has also created high tolerance for false alarms.

There has, however, been a steady increase in other U.S. government customers seeking the IC’s analytic support (Fingar, 2009), extending far beyond the military and other traditional users. The new customers range from the 18,000 state, local, and tribal law enforcement units that now may want terrorism-related intelligence (Perrelli, 2009); to the U.S. Department of Health and Human Services, which wants disease-related intelligence; to the U.S. Agency for International Development (USAID) and others involved in emergency relief around the world. These customers ask different questions, require different intelligence support, and have different tolerance levels for false alarms and ability to plan for worst-case scenarios than the IC’s traditional military customers. Their questions require analyses on complex, interrelated domestic and foreign issues; with players from multiple countries and nongovernmental entities; and with a wide range of political, economic, social, and technical dimensions. These questions may need different perspectives than the more traditional transnational and country-specific perspectives (e.g., whether North Korea’s nuclear weapons program is viewed as a proliferation problem based in North Korea or as a North Korea problem with a nuclear dimension). In addition to meeting the needs of these new customers, the IC must simultaneously continue to meet the mission support needs of the Department of Defense (to which 8

of the 16 IC agencies belong), wherever and whatever they may be (e.g., from counterterrorism to disaster relief anywhere on the globe).

How well the IC meets these needs depends on the human capital embodied in its people and processes. The IC must recruit, select, train, motivate, and retain the right workforce, whose members must be adept at locating information, identifying potential collaborators, tapping expertise (inside and outside the IC), and using good analytic tradecraft. In order to support these needs, the IC has created such innovations as Intellipedia, A-Space, the Analytic Resources Catalog, and the Library of National Intelligence. 1 In addition to these tools, internal deliberations on the best analytic techniques for different classes of problems, as well as deliberations about the individuals and procedures needed to apply them, are necessary to cultivate analytic skill.

These are all human activities, requiring expertise that resides in the behavioral and social sciences. 2 These sciences include the scientific study of understanding, judgment, and collaboration and communication, within and across organizations. The remainder of this report deals with the opportunity to take advantage of this scientific knowledge to review current IC practices and develop improved ones. The committee is grateful for the invitation to apply the accumulated expertise of these sciences to the IC’s challenges and initiatives.

Open Sources

The role of open sources in intelligence analysis demonstrates the analytical changes that the behavioral and social sciences can inform. The IC has long recognized the value of open source intelligence (OSINT). From 1941 to 2004, the Foreign Broadcast Information Service (FBIS) provided near real-time translations and republication of articles, speeches, and writings from foreign sources, giving information to intelligence officers, others in the U.S. government, reporters, and scholars. Since 1957, the U.S. Joint Publication Research Service has translated and published unclassified writ-

ings from around the world into English. In 2005, ODNI’s Open Source Center (OSC) absorbed and expanded FBIS’ capabilities distributed through its online World News Connection.

These services position the IC to benefit from the explosion of open-source information, especially for access to networked and cell-based threats. Nonetheless, there is still an ongoing debate about its value relative to clandestine information. Skeptics argue that “the intelligence community’s principal mission is to discover and steal secrets; relying on open sources runs counter to that mission” (Best and Cumming, 2007, p. 4; also Lowenthal, 2009; Mercado, 2005; Sands, 2005; Steele, 2000; Thompson, 2006). This position may reflect both experience and the intuitive tendency to place greater value on narrowly held information (Spellman, 2011).

Contrary to this skepticism, multiple government commissions have uniformly advocated greater use of OSINT (e.g., Commission on the Roles and Capabilities of the United States Intelligence Community, 1996; National Commission on Terrorist Attacks upon the United States, 2004). In its call for sweeping changes in the IC, the Intelligence Reform and Terrorism Prevention Act of 2004 described open information as “a valuable source that must be integrated into the intelligence cycle to ensure that United States policymakers are fully and completely informed” (Section 1052.a.2.). A few years later, the DNI Open Source Conference 2008: Decision Advantage convened participants from across the open source community to look at the spectrum of open source issues and best practices. 3 Box 1-2 provides four noteworthy quotations from the debate over the use of clandestine versus open sources.

All these claims embody assumptions about analysts’ ability to extract and evaluate information from different sources. Open sources can be particularly useful when analyzing human behavior, such as economic, political, religious, and cultural developments. Moreover, open sources can strengthen the analytical process itself by providing cross-checks on information from clandestine sources and testing the soundness of common wisdom or emerging consensuses. The behavioral and social sciences provide a disciplined way of evaluating such assessments, complementing the intuitions and personal experience that inform them, as well as empirically evaluating their actual performance. The need for such science arises from the inevitable fallibility of human judgment and organizations.

CHALLENGES FOR COMPLEX ORGANIZATIONS

Any organization that operates in a complex, fast-paced, high-stakes environment must find ways to learn and adapt, by shaping its personnel, organizational structure, and institutional culture to that changing reality. The IC shares many of the characteristics, strengths, and pathologies of other complex organizations. As a result, despite its unique mission and constraints, the IC stands to learn from research conducted in other settings on how to learn from experience, encourage collaboration, and improve communication with its customers.

Learning from Experience

The IC’s quickly changing, complex world makes it vitally important that it be able to learn from experience. However, as psychologists know (e.g., Brehmer, 1980), learning from experience is much harder than it seems.

One barrier is securing systematic feedback regarding analytical performance. Research has shown that outcome feedback is vital to correcting errors and reinforcing accurate performance (e.g., Kluger and DeNisi, 1996). However, IC analysts make predictions for events far in the future without the opportunity for feedback on how well they did and what factors account for their successes and failures. A second barrier arises from changes in world conditions that occur after analyses are made, some of which may be prompted by the analyses themselves (e.g., when national leaders take warnings seriously and act on them). Both the analysts and their customers must evaluate the analyses based on what they would have been if change in the world had been considered. Such counterfactual judgments face obvious challenges.

The behavioral and social sciences have developed ways to address these problems through statistical analyses of multiple forecasts. Such evaluations are common in medicine, which faces similar difficulties with long time frames and changed conditions. Done well, they can provide a picture not available with individual analyses. Sometimes, they show surprising results. For example, although weather forecasters are often criticized, their probability forecasts in the aggregate are accurate (for a review, see Murphy and Winkler, 1984). Decision makers who know about that accuracy (e.g., farmers, military planners) use them as a valuable input to their decision making. The forecasters’ accuracy reflects both their knowledge about the weather and their working in organizations that provide them with useful feedback and evaluate them fairly. The need to quantify confidence has been faced by members of other high-stakes professions, including medicine and

finance. This report examines the implications of this research for the seemingly similar problems faced by the IC’s analysts and customers.

One impediment to such learning is people’s unwarranted confidence in their own judgment and decision making (Slovic et al., 1972; Wilson, 2002). A large body of research also documents gaps between how people explain their decisions and statistical analyses of the processes that drive them. Quite often, as a result, people neither see the need for change (because they exaggerate how well they are doing) nor are able to make good use of experience. Thus, exhorting analysts to rely more on one factor and less on another means little if they misunderstand how much they are currently relying on those factors. Research with other high-stakes professionals finds troubling tendencies for experience to increase confidence faster than it increases performance (Dawson et al., 1993) and for people to exaggerate how well they can overcome conflicts of interest (Moore et al., 2005).

Scientific studies have identified other impediments to learning from

experience (many detailed in subsequent chapters and in the committee’s companion volume, National Research Council, 2011). One prominent example is hindsight bias, the exaggerated belief after an event has occurred that one could have predicted it beforehand (Arkes et al., 1981; Dawson et al., 1988; Fischhoff, 1975; Wohlstetter, 1962). Analyses following Pearl Harbor, the 9/11 attacks, and other prominent events often lead to the conclusion that they should have easily been anticipated, had there not been a “failure to connect the dots.” However, research finds that accurate prediction is much harder than it seems in hindsight. The “failure to connect the dots” metaphor is itself a corollary of hindsight bias, which can complicate learning from experience by leading people to overlook other sources of failure, such as not collecting needed information or communicating it clearly.

A complement to hindsight bias is outcome bias, the tendency to judge decisions by how they turned out, rather than by how thoughtfully they were made (Baron and Hershey, 1988). However accurate an analysis, the

analysts cannot be held responsible for the decision makers’ actions that follow, unless they have failed to communicate the analysis effectively, including the confidence that should be placed in it. Research has documented these biases, the efficacy of different ways of overcoming them, and the methods for ensuring that analysts and policy makers are judged fairly when making tough calls in uncertain environments.

A third impediment to learning from experience is the “treatment effect” (Einhorn and Hogarth, 1978). Often, predictions lead to actions that change the world (a “treatment”) in ways that complicate evaluating the prediction. For example, a prediction of aggression may be wrong, but it may lead to actions that provoke aggression that would not otherwise have happened, thereby falsely confirming an inaccurate prediction.

As discussed below, the IC is acutely aware of the need to learn and adapt. Behavioral and social research provides mechanisms for evaluating the theoretical soundness and the actual performance of current and potential methods to foster good analytic judgment.

Collaboration and Communication

It is widely recognized that increasing collaboration and communication is key to the IC’s success in a rapidly changing, complex world. The mission statements of IC entities show the emphasis that the IC leadership places on these capabilities, 4 as do its investments in innovations such as Intellipedia and A-Space. When the IC is the target of public criticism, the error most commonly cited is failure to communicate, within itself and with its customers. This was the case with the 9/11 attacks and more recently with the failure to warn of the Christmas Day 2009 bombing attempt. President Obama’s homeland security adviser, John O. Brennan (2010) said, “We could have brought it together, and we should have brought it together. And that is what upset the President.”

One of the biggest challenges in improving collaboration and communication within the IC is its organizational structure. As noted, the existence of 16 separate intelligence agencies (in addition to the ODNI) is a natural consequence of the specialized knowledge that each agency needs. However, these organizational structures create “silos” or “stovepipes” with boundaries that impede collaboration and communication.

These problems, too, are not unique to the IC. The failure to prevent the 1986 Challenger disaster stemmed from the inability of various subunits

in the National Aeronautics and Space Administration to integrate what each knew and from their different methods for processing information (Zegart, 2011). Research has identified these and other organizational factors that can impair information integration, as well as the efficacy of ways to overcome them. These barriers include the need for secrecy, “ownership” of information, everyday turf wars, intergroup rivalry, and differing skill sets—none of which is unique to the IC. For example, research shows how close-knit groups can become so homogeneous that they do not realize their limits to their in-group perspectives. Indeed, the IC has begun several efforts to overcome these barriers and to take advantage of its distributed expertise. Here, too, research has resulted in methods to evaluate the theoretical soundness of these measures, to evaluate their success, and to develop improvements (e.g., Lawrence and Lorsch, 1967; Weick, 1995).

CHARGE TO THE COMMITTEE

The IC recognizes that throwing more money and people at problems or exhorting analysts to work harder will not meet its challenges. The only viable course of action is to work smarter. The Intelligence Reform and Terrorism Prevention Act of 2004 created new opportunities to reduce organizational impediments to working smarter by empowering the Director of National Intelligence to transform the IC from a collection of semiautonomous special-purpose organizations into a single integrated enterprise.

In response to the need to explore new analytic processes and practices for the IC, the Office of the Director of National Intelligence asked the National Research Council to establish a committee to synthesize and assess evidence from the behavioral and social sciences relevant to analytic methods and their potential application for the IC: see Box 1-3 for the full charge. This report, along with a companion collection of papers, Intelligence Analysis: Behavioral and Social Scientific Foundations , is the committee’s response to that charge. Our report focuses on strategic analysis at the national level, although many of its findings may apply to combat environments where tactical or actionable intelligence may receive a higher priority or emphasis than strategic intelligence. Due to the unique circumstances of analysts, collectors, and decision makers often working side-by-side in combat environments, that application requires separate work beyond the scope of this committee’s charge. The same is true for analysis of the institutional structure of the IC. Our recommendations are meant to improve the quality of analyses within the constraints of the current structure.

Framed by this chapter’s introduction to the challenges for the IC, the rest of this report presents the behavioral and social science knowledge that can improve intelligence analysis. Chapter 2 looks broadly at two tasks central to the work of the IC, learning and evaluation. Chapter 3 identifies a

suite of proven scientific analytical methods available for application within the IC. Chapter 4 addresses the human resource policies needed to recruit, select, train, motivate, and retain employees able to do this demanding work. Chapter 5 considers how to optimize internal collaboration, allowing analysts to share information and learn from one another, thereby making best use of the community’s resources. Chapter 6 considers the communications needed for customers to inform analysts about their changing needs and for analysts to inform customers about the changing world. The final chapter presents the committee’s recommendations. The committee’s companion volume offers more details on the research summarized in this consensus report. The companion volume is designed to be suited to individual reading or courses incorporating the behavioral and social sciences in the work and training of intelligence analysts.

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The intelligence community (IC) plays an essential role in the national security of the United States. Decision makers rely on IC analyses and predictions to reduce uncertainty and to provide warnings about everything from international diplomatic relations to overseas conflicts. In today's complex and rapidly changing world, it is more important than ever that analytic products be accurate and timely. Recognizing that need, the IC has been actively seeking ways to improve its performance and expand its capabilities.

In 2008, the Office of the Director of National Intelligence (ODNI) asked the National Research Council (NRC) to establish a committee to synthesize and assess evidence from the behavioral and social sciences relevant to analytic methods and their potential application for the U.S. intelligence community. In Intelligence Analysis for Tomorrow: Advances from the Behavioral and Social Sciences , the NRC offers the Director of National Intelligence (DNI) recommendations to address many of the IC's challenges.

Intelligence Analysis for Tomorrow asserts that one of the most important things that the IC can learn from the behavioral and social sciences is how to characterize and evaluate its analytic assumptions, methods, technologies, and management practices. Behavioral and social scientific knowledge can help the IC to understand and improve all phases of the analytic cycle: how to recruit, select, train, and motivate analysts; how to master and deploy the most suitable analytic methods; how to organize the day-to-day work of analysts, as individuals and teams; and how to communicate with its customers.

The report makes five broad recommendations which offer practical ways to apply the behavioral and social sciences, which will bring the IC substantial immediate and longer-term benefits with modest costs and minimal disruption.

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The Analytic Edge: Leveraging Emerging Technologies to Transform Intelligence Analysis

Photo: Adobestock

Photo: Adobestock

Table of Contents

Brief by Brian Katz

Published October 9, 2020

Available Downloads

  • Download the CSIS Brief 715kb

CSIS Briefs

  • How well and how rapidly the intelligence community (IC) integrates emerging technologies into all-source analysis will be vital to its ability to generate timely, relevant, and accurate strategic insights and to sustain policymakers’ decisionmaking advantage over capable rivals.
  • Artificial intelligence (AI) and associated technologies cannot replicate all the complexities of crafting strategic analysis but can automate, enhance, and enable key parts of the analytic process and be used to unlock new insights to inform analytic judgments.
  • AI can assist analysts in streamlining and sensemaking of exponentially growing intelligence data. With fewer tasks, better data, and machine-derived insights, analysts will have more strategic bandwidth to apply their expertise and deliver high-level analysis to policymakers.
  • To harness advanced technologies, IC analysts must overcome a host of challenges, barriers, and limitations —in the underlying data, algorithms, and ultimately the analysts themselves.
  • IC leaders and stakeholders—policymakers, Congress, the technology, and research sectors—must provide the analytic workforce the technology and training to thrive today while laying the digital groundwork, institutional priorities, and cultural norms for future success.

INTRODUCTION

If the United States invests now in technology transformation, the intelligence analyst of 2030 will be able to look back at the analyst of 2020 with incredulity and even pity. Armed with world-leading AI, cutting-edge data analytics, and unlimited cloud computing power, the analyst will maintain almost continual awareness of their target operational environment. They will rapidly surface, fuse, visualize, and action high-quality data across the information spectrum, from open-source to highly classified. They will deliver high-level, data-rich, quick-turn insights to their policy customers. The analyst will shrug at their predecessors’ ad hoc adoption of technology and antiquated “read, write, think” analytic process as relics of a bygone era that is incompatible with the speed and scale of big data.

The analyst of 2020 has neither the time nor inclination to ponder this seemingly fantastical future. As data grows exponentially, their capacity to process it grows marginally. Their monitors are filled with multiple intelligence queues, siloed share drives, manually curated spreadsheets and databases, and error-riddled .kmz files, with no interface to synthesize the data. They are overwhelmed by myriad new tools and “AI solutions” now offered to them and underwhelmed with their utility and suitability for strategic analysis. With unrelenting customer demands and timelines, the analyst defaults to their small set of trusted compartmented sources and time-tested tradecraft to assemble their evidence and deliver a “good enough” intelligence product that is more or less on time.

While the current picture is not as bleak and the future likely not as optimized as above, IC analysts in 2020 are flatly behind the technology curve. The explosion of data and disruptive technologies, rapid evolution and emergence of new global threats, and accelerating policymaker decision cycles will likely upend the intelligence analysis process. How well and how rapidly the IC integrates advanced technologies into all-source analysis will be vital to its ability to compete in future intelligence environments and deliver timely, accurate, and relevant analytic products.

While envisioning and building towards the analyst of the future, the IC can and must harness emerging technologies to empower analysts today. In part two of its three-phase study, the CSIS Technology and Intelligence Task Force explored how technologies such as AI 1 and its subset machine learning (ML), 2 cloud computing, 3 and data analytics can empower intelligence analysis. Building off phase one of the study, which focused on intelligence collection, the core research question guiding phase two was what are the opportunities and limitations of emerging technologies for strategic intelligence analysis ? To answer it, the Task Force convened stakeholders and experts from across the IC, technology, policy, and research communities for a series of discussions.

This CSIS research brief summarizes the main findings from the second phase of the Task Force. The brief begins by studying the near-term ways technologies can be integrated into the analytic process. It then assesses the key barriers and limitations to integrating AI and other technologies into strategic analysis. The brief concludes by exploring where technology and analysts will provide the most value to policymakers and the implications for building the IC of the future.

Opportunities: Creating Strategic Bandwidth

“The IC’s job,” as the ODNI AIM Initiative describes it, is to “analyze data, connect disparate data sets, apply context to data, infer meaning from data, and ultimately make analytic judgments based on all available data.” 4 The problem, however, for analysts today is that “the pace at which data is generated, whether by collection or publicly available information, is increasing exponentially and long ago exceeded our collective ability to understand it or to find the most relevant data with which to make analytic judgments.” If analysts cannot process, absorb, and integrate the right data, they cannot turn it into coherent, insightful, and compelling analysis for their customers.

AI and associated technologies cannot replicate all the complexities of crafting strategic analysis but can automate, enhance, and enable key parts of the process and be used to unlock new insights to inform analytic judgments. These technologies can help optimize intelligence flows, automate mundane but vital processing tasks, augment analysts’ sensemaking and critical thinking skills, and even perform certain types of analysis. Emerging technologies can, in short, create more strategic bandwidth for analysts to think and write strategically. With more time, fewer tasks, better data, and new ways to generate insights, analysts will be more able to apply their unique expertise and deliver high-level, quick-turn, valued-added analysis to policymakers.

Optimizing Intelligence Traffic : The volume and variety of intelligence coming into analysts’ “traffic” queues—from sensor data to signals intercepts to diplomatic cables to social media—has far surpassed what they can process. AI and analytics tools can help optimize information flows and augment and enrich them to gain more insight from less data in a fraction of the time.

  • Prioritized : AI capabilities could be applied to triage and surface the most relevant and useful information prioritized by analysts, automating the time-intensive task of intelligence traffic curation. 5 AI tools could also be trained to scan, spot, and flag information analysts designated as critical or anomalous and prioritize it in their queues, providing early indicators and warning of new developments for analysts to alert policymakers. 6
  • Personalized: As AI-enabled traffic models learn analysts’ preferences, AI prediction tools and recommendation algorithms could be used to find and flag reporting of interest based on the analyst’s portfolio and search history (e.g., “If you liked that SIGINT intercept on Adversary Y, you might like this HUMINT report from Station X”). 7 ML models could be honed for better customization over time, learning how analysts move through data and value certain report attributes which would lead to better recommendations. 8 Such reinforcement learning could extend across teams and agencies, leveraging cloud and collaborative filtering. 9
  • Summarized : Advances in ML, particularly natural language processing (NLP), 10 could enable algorithms to comprehend and summarize large and growing bodies of unstructured text in intelligence traffic—HUMINT reports, processed SIGINT and imagery reports, diplomatic cables, and open-source—to trim and streamline traffic. 11 ML tools could help compress and even compose report summaries while identifying and clustering topics or entities of interest, enabling analysts to scan or dive deep into the reports as time allows. 12
  • Enriched : AI tools could be used to not only summarize intelligence but also to augment and enrich it, using automation to embed metadata such as time, location, actors, and events. Data enrichment could enable analysts to derive more information and context from each report and build connections across all reports. 13,14
AI and analytics tools can help optimize information flows and augment and enrich them to gain more insight from less data in a fraction of the time.

Smarter Search and Organization : After curating analysts’ daily traffic, AI tools can assist analysts in how they search, organize, and begin making sense of relevant reporting. Technology can help analysts pose optimal questions, search the right datasets, and automate how data is collated and cataloged. By automating mundane but vital analytic tasks and better sifting “signal from noise,” AI tools could enable analysts to move faster from structuring to sensemaking of data.

  • Intelligent Queries: With AI, analysts could hone smarter questions and search algorithms for a given intelligence question, casting wider and more efficient nets across datasets to piece together critical but often nonexplicit information (e.g., what is adversary X’s strategy for Y?). Analysts could team with data scientists to tailor how data is tagged (e.g., words associated with “strategy”) and how queries are sequenced to enable algorithms to learn and launch more complex or indirect searches. 15 NLP applications could help summarize and structure the search findings based on specific analytic needs. 16
  • Targeted Sifting and Surfacing: With AI-tailored queries, analysts should be able to leverage ML tools to search and sift across vast and various datasets and reporting streams to surface intelligence of value. As results are filtered, NLP tools could help detect, extract, summarize, and gather prioritized information and topics—such as people, locations, organizations, and events. 17 Such sifting and surfacing capabilities could augment day-to-day traffic monitoring and searches but would be vital during times of crisis, helping analysts focus the intelligence flow, find the best data, and respond rapidly to policymakers’ needs. 18
  • Automated Curating and Cataloging: Having gathered the right intelligence, analysts could exploit ML tools to automate the cataloging and organization of that intelligence. Instead of manually populating and integrating data spread across spreadsheets, databases, and .kmz files, analysts could harness NLP to generate “knowledge bases” that cluster, curate, and populate reporting into analysts’ unique frameworks and ontologies. 19 Knowledge bases could be further automated to continuously analyze and self-update with new intelligence reports. 20

Augmented Sensemaking and Detection : With intelligence sifted and streamlined, AI tools and advanced analytics could help analysts make sense of it, augmenting their ability to identify and visualize patterns, relationships, and change in their target environments in real time. Analysts could be able to leverage AI and cloud to maintain enhanced, persistent, and machine-updated situational awareness of their target.

  • Patterns and Networks: With datasets pooled and integrated on cloud-based data lakes, deep learning algorithms 21 could be deployed to find patterns, trends, and relationships that would be impossible for analysts manually reviewing the data to identify. 22 Analysts and data scientists could team to develop and guide ML models harnessing neural networks to classify, cluster, and connect data into nodes and networks. 23 Applying ML to graph data—data that can be connected, like people, organizations, locations, and events, as nodes in a network—could enable analysts to not only identify relationships and networks but infer judgments on the key influencers and the nature of the connections, revealing new insights or validating initial assessments. 24
  • Knowledge Visualization: After identifying patterns and networks, analysts could leverage AI to visualize them for enhanced clarity, meaning, and integration into their analysis. Synthesizing data from multiple data streams and analysts’ internal knowledge base, AI tools could visually capture new intelligence and changes in their target environment. 25 With analyst-friendly interfaces, analysts could “see” their intelligence in compelling and manipulable formats and integrate it into creative products for their consumers.
  • Enhanced Situational Awareness: AI and data visualization could enable analysts to eventually maintain a near real-time picture of adversary activity across multiple domains, providing a common operating picture (COP) that keeps pace with changes in the operating environment. Automating and orchestrating data display from various sensors, collection streams, and open-source intelligence (OSINT) in one interface could enable analysts to capture and monitor signals and data of new activity and establish patterns and baselines of what is routine and normal. An enhanced COP over time could provide a wholistic, dynamic accounting of U.S., friendly, neutral, and adversary activity and continual assessments of change for policymakers.
  • Detecting Anomalies and Incremental Change: As analysts and machines team to establish baselines and expectations of adversary activity, AI tools could then surface anomalous behavior and detect weak but important signals and deviations to flag and direct analytic focus. By integrating analytic “tripwires” into monitoring frameworks, analysts could exploit AI for real-time monitoring of meaningful incremental changes otherwise missed in the daily intelligence churn and which can culminate later in strategic surprise and intelligence failures.

Offloading Analysis and Harnessing OSINT : AI tools could not only automate and enhance processing and sensemaking tasks for analysis but could also perform certain types of analysis. IC analysts can harness these tools and the growing availability, quality, and relevance of OSINT both to generate inputs and machine-derived insights for their analysis as well as to offload or outsource analytic work done as ably or better by machines.

  • Geopolitical and Battlefield Updates : Analysts are called upon to craft daily “intelligence” products that update policymakers on political and military developments in conflict zones that are just summaries of media and other OSINT reports due to lags in classified collection. Analysts could leverage AI, particularly NLP, to cull the same data, summarize findings, and generate written summaries for analysts’ updating, fine-tuning, and additional context. 26
  • Stability and Crisis Monitoring : Analysts could leverage AI-enabled data mining, sentiment analysis, and geolocation tools to help monitor and predict disruptive events—from mass protests to pandemic outbreaks—for early warning of potential crises and instability. When combined with data sifting, visualization, and NLP foreign translation tools, advanced OSINT capabilities could provide analysts a rapid and accurate initial assessment of global flashpoints and key indicators for where to steer classified collection. 27
  • Political and Economic Forecasting : Advanced OSINT could supplement or even substitute for all-source analysts in areas where the IC has a mixed tracked record of performance and unclear comparative advantage, such as predictive analytics and long-range geopolitical and economic forecasting. 28 Analysts could leverage their historic and classified knowledge of adversaries and machine modeling and compute power to generate sophisticated scenarios analysis, identifying high-impact but previously unforeseen scenarios and predictions.

Honing Analytic Lines: As analysts build their analytic lines, assembling key evidence and forming initial judgments, they could harness cloud, AI, and data analytics to refine and test their analysis against machine-derived and IC-wide insights. While unable to replicate an analyst’s cognition, contextual knowledge, and critical thinking, AI can test and strengthen their analysis by surfacing contrary data, measuring historic accuracy, and positing alternative hypotheses.

  • Extended Intelligence Collaboration: Cloud and AI tools can enable analysts to coordinate and collaborate more effectively from start to finish in the analytic process, from sharing and developing datasets and algorithms to jointly authoring products. 29 Analysts could reenvision coordination from simple product review to a collaborative process of extended intelligence , harnessing distributed human expertise with machine power to generate insights. 30 Collaboration could be extended not only across the U.S. IC but also with foreign liaison partners.
  • Testing Analytic Lines: While building assessments, analysts can use AI and advanced analytics to test assumptions, hypotheses, and initial judgments against big data and algorithm-derived results. Corroboration could strengthen analytic lines while alternative findings push analysts to revisit their evidence, logic, and conclusions. As confidence in algorithms grows, analysts could use machine-derived findings to not only vet their analysis but inform it, leveraging new insights from data.
  • Overcoming Analyst Bias: Machine knowledge and judgment of past analytic lines, source veracity, and competing hypotheses could add more rigor to analytic process while helping analysts confront bias and groupthink. AI could surface anomalous, undervalued, and countervailing reports that analysts trusting a small, compartmented source base may have missed or discounted. 31 Contrary analysis can help analysts overcome confirmation and anchoring bias on established analytic lines and instill more transparency in analysts’ levels of confidence. 32
While unable to replicate an analyst’s cognition, contextual knowledge, and critical thinking, AI can test and strengthen their analysis by surfacing contrary data, measuring historic accuracy, and positing alternative hypotheses.

Enabling Analytic Disciplines and Missions : AI and advanced analytics can be directly applied and integrated into core analytic disciplines and missions. These technologies can inform and enhance long-standing tradecraft in counterterrorism (CT), military, and political analysis and science, technology, and information warfare analysis rapidly rising in importance.

  • Targeting and Network Analysis : AI, data analytics, and intelligence fusion tools could enable advanced network analysis for targeting operations and anomaly detection for “needle in the haystack” analysis vital to CT and other operational intelligence. ML and graph analytics could enhance any network-centric analytic mission, such as countering weapons procurement and proliferation, illicit trafficking, sanctions evasion, and transnational crime.
  • Military Analysis: AI and multi-INT fusion and visualization tools could enable military analysts to build dynamic order-of-battle of foreign militaries and near real-time battle-tracking, bolstering insights on adversaries’ strategic capabilities and current operations, including clandestine and irregular warfare activity. 33 AI and cloud could power advanced and more realistic war gaming and simulations. Deep learning and NLP could incorporate diverse datasets on adversary decision variables—from geospatial and logistics data to political intelligence and military doctrine—and integrate into decision models and courses of action. 34 Advanced simulations and modeling could augment war games of future scenarios but also day-to-day assessments of adversaries’ near-term behavior and courses of action.
  • Political and Leadership Analysis: ML, graph analytics, and data visualization tools could help political analysts structure and understand influence networks, “inner circles,” and decisionmaking processes of foreign government leaders. Advances in sentiment analysis could allow analysts to better anticipate political trends and influencers shaping foreign decisions, while individual attribute modeling could help predict leaders’ potential responses to various U.S. policies. 35
  • Science and Technology (S&T) Analysis: Much as analysts have long analyzed foreign weapons systems and defense industries, understanding foreign S&T innovation and integration into military and intelligence missions will be a top priority for IC analysis and policy customers for the foreseeable future. As IC analysts become more knowledgeable and adept at employing AI and other emerging technologies, their expertise could enable deeper insights and analysis of adversaries’ S&T plans, intentions, capabilities, and threats. Tech literacy will be vital in assessing foreign cyber, disinformation, and influence campaigns and the implications of next-generation technologies, such as biotechnology and quantum computing.

LIMITS AND BARRIERS TO ADOPTION

While the benefits of AI and associated technologies could be immense for analysts, the IC faces several key obstacles and limitations in adopting and applying these tools to all-source analysis. The broad challenges of technology acquisition, digital infrastructure, and data architecture identified in phase one of the Task Force as hampering intelligence collection and processing missions will impact analytic missions as well. But structural barriers are neither the only nor often the primary obstacle. Prevailing against analytic adoption of emerging technologies is the technologies’ own limitations in meeting analysts’ standards of tradecraft and explainability, and the cultural and institutional preferences of analysts and agencies for their traditional approach to intelligence and analysis.

Capturing and Integrating Data : Accurate and insightful application of AI requires capturing, cleaning, and curating the right data. The sheer volume of potentially relevant intelligence and data for analysis may surpass the capacity of even the most tech-enabled analysts to process, filter, and absorb. Further complicating the harnessing of data will be the challenges of standardizing and integrating the best datasets, both collected from classified means and surfaced from the open source.

  • Keeping Pace with Data: The speed and scale of IC integration of intelligence and data processing tools must keep pace with accelerating volumes and diversity of intelligence and data. Even with AI-enabled optimization and streamlining, the proliferation of sensors, intelligence streams, and OSINT data—accelerated by 5G and IoT devices—could still overwhelm analysts’ capacity to process. Inability to capture and analyze real-time data will leave IC analysts behind the curve in providing situational awareness to policymakers.
  • Integrating Data: The best AI applications for analysts would harness both classified and OSINT data in training algorithms and deriving insights, but incompatible architecture and security barriers could hamper “low-side”/“high-side” data integration. Along with data, ML algorithms and models honed on open source could face similar obstacles being migrated onto classified systems and integrated into analytic workflows.
  • Tagging Data: The best AI applications also require massive amounts of data, which in turn require extensive labeling and tagging—a tedious, time-consuming, and still primarily human task. 36 Unlike the private sector, which can crowdsource and employ “gig economy” taggers, the IC’s classified datasets require labeling to be done internally and mostly manually by cleared analysts and contractors. While perhaps sufficient in the short term, manual labeling and tagging will be untenable as data continues to exponentially grow. 37

Algorithmic Limits : Analysis depends on rigorous tradecraft and clear explanations and reasoning for the logic, evidence, assumptions, and inferences used to reach conclusions. The complexities of strategic analysis, standards and requirements for transparency and intelligence assurance, and the inherent challenges of modeling analytic processes and performance will create theoretical and practical limits to applying current AI capabilities to analytic workflows.

  • Modeling the Strategic: The complex tradecraft and cognitive skills of strategic analysis is innately difficult to define, standardize, replicate, and thus model, creating practical limits for AI applications. Contextualizing and recognizing implications of new intelligence, weighing and connecting data to form an intelligence picture, organizing intelligence logically and persuasively into argumentation and assessments—the analytic process is a blend of art and science, standardized tradecraft and individual heuristics, and judgments derived from hard data and deductive reasoning and from cultural expertise and analytic intuition honed over time. If analytic tasks cannot be defined digitally, the ability to apply AI will be limited. 38
  • Bias: Generating insights from AI requires analysts to help shape, hone, and steer algorithms and models, but analysts introduce bias in how they conceptualize the intelligence problem, design the model, and select data for input, leading to biased and potentially inaccurate results. Transparency of biases inherent in the data, how models are used, and their impact on conclusion and confidence levels will be vital but may not easily be understood by customers. 39
  • Explainability: To use AI-derived findings, analysts will need to know the logic, bias, assumptions, and inferences of the algorithms and models used to generate them—which may or may not be knowable. Many of the most sophisticated AI applications and machine insights derive from “black box” algorithms in which machine logic and processes are hard if not impossible to define. A lack of transparency on evidence chains, where and how AI was used, and validity conditions means machine findings will be untrustworthy and unusable. 40
  • Authenticity: Analysts must continue to evaluate intelligence for its quality, accuracy, and relevance while learning how to measure a new factor once taken for granted: authenticity. Deception techniques to fool algorithms into misclassifying data and use of generative adversarial networks 41 to create deepfakes of classified and open-source data could sow confusion among analysts, leading to poor analysis and misinformed policy decisions. 42 Ensuring data and intelligence authenticity will only grow more difficult as adversaries become more adept at altering data and waging targeted disinformation campaigns at speed and scale.
  • Security: Analysts will also face aggressive and targeted adversarial AI efforts from hostile foreign intelligence services aimed at penetrating and undermining AI systems—and with it, analysts’ confidence in AI tools and results. The rush to adopt AI could come at the expense of rigorous AI security standards, protocols, and testing requirements, creating vulnerability to a range of “counter-AI” threats, from “poisoned” data injected into AI models to fully hacked and manipulated systems. 43 Even if adversaries cannot gain such a level of access, convincing analysts their AI is compromised and unusable could achieve the same effect. 44

Analytic Aversion to Change : While the technical obstacles are significant and real, perhaps the greatest barrier to AI adoption could be analysts themselves. Deeply embedded in the analytic community are institutional, bureaucratic, and cultural preferences and bias toward the time-tested tradecraft and techniques they perceive to be the global gold standard. Underinvestment in digital acumen, uncertainty of AI and OSINT’s mission value, and cultural aversion to risk and change could hinder even the most innovative analysts and units from integrating emerging technologies into their mission.

  • Digital Literacy: Analysts will need baseline digital skills to effectively harness AI and analytics tools in their analysis and to explain AI-derived findings to even less digitally savvy policy customers. To develop those skills, analysts will need not only specialized training but supportive leaders and management that value and incentivize it. Agency leaders, however, will need to balance investment in digital proficiency with traditional tradecraft, language, and other regional-specific training that, too, will remain vital to the IC’s analytic advantage.
  • Bureaucratic Disincentives: AI investments require multiyear commitments to see through adoption and integration, managerial expenditure of social capital to gather institutional support, and leadership’s acceptance of risk and occasional failure. Managers, however, are often only in their positions for 2-3 years and may be unwilling to spend their already strained time and resources on new technologies with uncertain mission payoff and a chance of failure, particularly if their IC leaders and oversight bodies do not incentivize such risk-taking.
  • Mission Value: Training, incentives, and leader support may still not be enough to spark tech adoption if analysts and managers see no clear and substantial “mission gain” from the technology. Marginal gains in insights and productivity may not justify the time, expense, and opportunity cost required to gain AI and analytics proficiency. Analysts may also be offered too many technical tools to see the value of any, particularly those not specifically designed and tailored to their unique analytic needs. Analysts with trust and confidence in traditional tradecraft are more likely to discard ill-suited technologies than conform to them.
  • Trusting the Nontraditional: Harnessing AI capabilities will require embracing OSINT as vital analytic input and learning to gain trust in machine-derived results. Blocking this embrace is an IC bias for classified reporting in forming judgments, skepticism of OSINT—only growing with deepfakes and disinformation—as diagnostic data, concern over AI security, and trust in time-tested tradecraft over black box processes. Preference for classified reporting may be appropriate, as a SIGINT intercept or HUMINT source may be the only way to discern plans and intentions. However, discounting timely, on-the-ground OSINT insight while waiting for clandestine ones to be collected and processed will leave analysts behind and outside policymakers’ information and decision cycle.

OUTLOOK : TECHNOLOGY’S—AND THE IC’S—STRATEGIC ANALYTIC VALUE

In weighing its benefits and limitations, it is clear that emerging technologies such as AI, cloud, and advanced analytics can create more strategic bandwidth for analysts by automating and enabling key analytic tasks. But what is technology’s value added to analysis itself? High-level analysis must answer the complex questions for policymakers (e.g., what are the prospects for conflict between an ally and rival power? Will mass protests in country X descend into civil war?). Answering them requires answering a series of interrelated sub-questions that must be connected into a coherent analytic story: what is happening and why , its impact , its outlook , and the implications for U.S. interests. Where can technology most assist in answering them?

Technology’s immediate value is in answering the what— to capture, curate, connect, and make sense of vast streams of intelligence and data on what is happening with an analyst’s country, issue, or target of interest. It can also help analysts assess impact —to detect and measure an issue or actor’s impact on the operational environment. Where AI and associated technologies lag is in answering the why. Understanding the drivers, intentions, and motives of foreign actors and the history, context, and personalities shaping their actions is primarily the realm of human experts. As AI technology advances, it may grow more able to determine these drivers and thus help predict and project the outlook   of where an issue is heading. Explaining the implications of intelligence for U.S. policymakers will remain the unique strength of the human analyst.

While emerging technology will provide immense value to IC analysis, another question will emerge in the years ahead: what is the value of IC analysis itself to U.S. policy? While the IC will still enjoy many advantages, namely in classified collection, the combination of high-quality OSINT, commercially available GEOINT and SIGINT, and data analytics will level the analytic playing field. Any trained and equipped organization will be able to generate all-source analysis of current events of comparable quality to IC analysts—at a faster pace and a fraction of the cost. In future information environments of ubiquitous sensing and continual awareness, the commercial sector’s faster technology adoption rates and superior facility with OSINT could give it the advantage over the IC in assessing what is happening in fast-moving global events.

IC analysts will likely have a diminished competitive advantage in delivering current intelligence to U.S. policymakers on current threats and events in the years ahead. But, to paraphrase another intelligence question, so what? As the IC aims to distinguish itself from open source, its value to U.S. policy will not stem from being a slightly better “classified CNN” analyzing current events. While the IC can and must deliver timely analysis to remain relevant to policymakers, the strength of the IC will remain the experience and expertise of its seasoned analysts and what they alone can provide policymakers: unique and unrivaled insight into the why , the outlook, and the implications of global events and emerging threats for U.S. interests.

Emerging technologies, of course, will still be vital. An IC analyst armed with the AI and OSINT to make rapid sense of what is happening and clandestine intelligence and historic context to know why will be able to provide unmatched insight on global threats, future scenarios, and the implications for U.S. policy. The combination of emerging technologies, human subject matter expertise, and IC tradecraft will leave IC analysts uniquely positioned to answer the types of vexing and often technologically oriented questions policymakers will pose in the coming years.

  • What’s new? As U.S. competitors increasingly adopt irregular, indirect, and clandestine approaches short of war to gain strategic advantage, analysts must be able to detect new and incremental “gray zone” activity in the political, paramilitary, information, and economic realms. 45 Analysts with AI-enabled signal detection, pattern finding, and visualization tools and expertise on adversary strategy, operations, and doctrine will be best positioned to spot new operations, discern incremental but meaningful change in operational environments, provide early warning to U.S. decisionmakers, and mitigate risk of strategic surprise.
  • What’s true? As foreign disinformation and influence campaigns accelerate—with greater speed, scale, sophistication, and seeming authenticity—policymakers will turn to the IC to help separate “truth from fiction.” Analysts will need AI capabilities such as in generative adversarial networks to detect synthetic and inauthentic deepfakes and sentiment analysis to measure influence operations’ impact. Analysts with baseline technical skills and country expertise will be ideally suited to assess adversaries’ information warfare strategy and potential future operations.
  • What’s next? Anticipatory strategic intelligence is less about predicting specific threats than envisioning and correctly assessing the likelihood of potential events and adversary actions. AI-enabled modeling, war gaming, and scenarios analysis could help analysts to discern and discover potential courses of action, predict adversary decision points, and identify signposts of low probability-high impact scenarios for U.S. interests before they occur.
An IC analyst armed with the AI and OSINT to make rapid sense of what is happening and clandestine intelligence and historic context to know why will be able to provide unmatched insight on global threats, future scenarios, and the implications for U.S. policy.

IMPLICATIONS: EMPOWERING THE ANALYST OF TODAY AND TOMORROW

The IC’s ability to integrate and leverage innovative technology for strategic analysis will be vital in generating and sustaining policymakers’ decisionmaking advantage over increasingly sophisticated adversaries and rivals. To maintain the analytic edge, the IC must simultaneously begin envisioning, planning, and resourcing the analytic missions of the future while rapidly embracing and assimilating emerging technologies into present-day tradecraft.

Bridging the continuum from current to future analytic needs will be the analysts themselves. With limited hiring, long lead times, small turnover, and high retention, the IC workforce cannot be easily refreshed and transformed with new technologically-savvy talent. 46 Indeed, “the workforce of the future,” as former CIA chief learning officer Joseph Gartin asserts, “is already here,” and the analysts of 2020 will be the leaders, managers, and, for many, still analysts of 2030. IC leaders and critical stakeholders—policymakers, Congress, and the technology and research sectors—must provide the analytic workforce the technology and training to thrive today while laying the digital groundwork, institutional priorities, and cultural norms for future success. How?

  • Embracing OSINT : The IC must reconceptualize OSINT as a foundational INT alongside traditional clandestine intelligence collection in informing and driving analytic judgments and a strategic necessity in a world of big data. Moreover, OSINT could serve as not only a vital input to classified assessments but also as an analytic mission in its own right. The combination of high-quality OSINT and commercial GEOINT and SIGINT means that all-source intelligence analysis can now be crafted at the unclassified level. Instead of viewing it as competition, the IC could embrace “finished OSINT” as an opportunity to expand its reach and impact to new customers and stakeholders likely to value IC tradecraft and insight but at the unclassified level, including domestic law enforcement, foreign governments, the technology and industrial sectors, and the broader U.S. public.
  • Elevating TECHINT : Intelligence of foreign AI systems and S&T capabilities, plans, and intentions must also be conceived as a foundational intelligence mission, spanning collection and analysis and essential for planning and resourcing future IC missions. The IC should be able to understand and forecast emerging technologies—particularly AI, biotechnology, and quantum computing—and their applications for foreign statecraft, economic competitiveness, and military and intelligence operations. Doing so will require both clandestine collection of adversary technological capabilities and applications and well-sourced OSINT of foreign S&T sector innovation, including patents, partnerships, acquisitions, and expansion. Analysts will need more technical and tactical knowledge to understand foreign AI systems as well as the capabilities and limitations of their own AI-enabled collection, targeting, and acquired data.
  • Integrating Technologists: Analysts will need to develop some level of digital acumen in data science and AI, but collaboration and teaming with true technologists—data scientists, ML engineers, and product designers—could unlock AI’s true potential for analysis. Integrating data scientists into analytic units will help data scientists understand the analytic problem set and analysts comprehend the underlying AI, enabling partnering to hone and tailor models, apply the right tools to the right datasets, and attribute meaning to results. 47 ML engineers and product designers will need access to the analyst end user to understand how to design, build, and adapt software, tools, and interfaces suited to analysts’ unique needs and requirements.
  • Proliferating Pockets of Success : While creating the digital infrastructure and institutional incentives for enterprise-wide technological adoption, IC leaders should empower individual directorates and mission centers to acquire, experiment with, and adopt the tools that fit their unique mission needs. Certain analytic missions, particularly more operational intelligence-focused ones such as CT, will be better suited to harness AI/ML. But IC leaders should identify the attributes, norms, and best practices of units embracing tech transformation and seek to proliferate the lessons learned to spur creative approaches across organizations.
  • Educating Policymakers : Analytic value ultimately derives from a product’s impact on policy customers and their trust in its analytic quality, clarity, and transparency in explaining its judgments. As the IC moves to integrate AI and data analytics into its products, analysts must be able to clearly and convincingly explain to policymakers how these technologies were applied, their relative weight in forming assessments, and their impact on confidence levels of key judgments. Analysts will need to become educators on AI and analytics applications and learn to build trust with the strategic leaders making critical policy and operational decisions based on their AI-enabled analysis.
The IC must reconceptualize OSINT as a foundational INT alongside traditional clandestine intelligence collection in informing and driving analytic judgments and a strategic necessity in a world of big data.

The IC’s embrace of emerging technologies could enable an even closer relationship between analysts and customers and help facilitate such enhanced analyst-policymaker interaction, which we will explore in phase three of the Task Force, focused on intelligence distribution.

Brian Katz is a fellow in the International Security Program at the Center for Strategic and International Studies (CSIS) and research director of the CSIS Technology and Intelligence Task Force.

This report is made possible by support to the CSIS Technology and Intelligence Task Force from Booz Allen Hamilton, Rebellion Defense, Redhorse, and TRSS.

CSIS Briefs are produced by the Center for Strategic and International Studies (CSIS), a private, tax-exempt institution focusing on international public policy issues. Its research is nonpartisan and nonproprietary. CSIS does not take specific policy positions. Accordingly, all views, positions, and conclusions expressed in this publication should be understood to be solely those of the author(s).

© 2020 by the Center for Strategic and International Studies. All rights reserved. Please consult the PDF for references.

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Dionex Inuvion IC System

the ic analysis

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Dionex inuvion ic system: ultra-reliable, day-to-day ion analysis.

Perform ion analysis with the Thermo Scientific Dionex Inuvion IC System, an intuitive, ultra-reliable ion chromatography (IC) instrument with straightforward operation and extended application capabilities. This system supports electrolytic suppression and can include Reagent-Free IC (RFIC) eluent generation for additional time-savings, greater day-to-day consistency, and gradients to optimize methods. It offers automated start-up and shut-down routines to save time and consistently reliable performance for busy analytical testing laboratories.

Flexible, adaptable platform

The Dionex Inuvion IC System is designed to help your lab meet the needs of today and the future. Upgrade the Dionex Inuvion IC System to include RFIC for automated eluent generation capabilities. Optional accessories can be swapped in and out of the instrument by users in less than five minutes for extended application capabilities.

Ultra-reliable performance

This state-of-the-art ion chromatography system is engineered for maximum productivity. With new pump technology and electronics, the Dionex Inuvion IC System provides enhanced day-to-day reliability. System self-diagnostics also detect any issues with hardware and consumables, giving you the confidence that it will be ready to run whenever you are.

Easy-to-use platform

The Dionex Inuvion IC System is designed for fast startup and maximum uptime. With convenient, unobstructed access to all necessary components and a logical flow path, users can quickly and safely access everything on the instrument.

Suppression compatibility

The Dionex Inuvion IC System offers the choice of using either chemical suppression, for more challenging samples, or electrolytic suppression, for ease of use and convenience. Suppressors remove conductive ions from the eluent to decrease background conductivity, while enhancing analyte signal, enabling high sensitivity to achieve low detection limits with conductivity detection.

High-pressure operation

With the ability to operate at up to 5,000 psi, the Dionex Inuvion IC System can take advantage of high-performance 4 µm-particle-size Thermo Scientific Dionex IC columns for more efficient separations that can be used to shorten run times.

Chromatography data system

The Dionex Inuvion IC System comes with the easy-to-use Thermo Scientific Chromeleon Chromatography Data System (CDS). This CDS platform automates and optimizes daily tasks and helps even inexperienced users achieve consistent, high-quality results.

the ic analysis

System specifications

The table below provides an overview of the Dionex Inuvion IC System specifications. Not all specifications are listed. For a full list of the specifications, download the Dionex Inuvion IC System specifications sheet .

Ordering information

Instruments, optional accessories.

For Research Use Only. Not for use in diagnostic procedures.

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IC analysis through tree diagram | IC analysis diagram | IC analysis examples.

Exemplify ic analysis through a tree diagram. | ic analysis diagram | ic analysis tree diagram | ic analysis examples | ic analysis limitations | ic analysis .  (nu '18,'14,'12).

(Short Points: Bloomfield, Definition of IC, Example, Tree Diagram, Tree Structure, Limitations of IC analysis.) Answer:                 IC analysis deals with immediate Constituent Analysis which is a method of sentence analysis. This method was first introduced by Leonard Bloomfield, pioneer of American structuralism.                     IC analysis is a technique of analyzing constituents of a sentence. It can be segmented from a phrase, clause or sentence. In IC analysis , a sentence is  broken up into immediate constituents. Divided constituents are also divided into further immediate constituents. This process continues until a meaningful unit of a word or a word is gotten.

                               For analyzing the IC ,we can keep an example , ''The player plays Football.'' First, it can be divided into the two ICs or single units, ''The players'' and "plays Football". Further, each of these part is divided as ''The'' and ''player'' wherein ''plays'' and ''Football''. According to this principle ,we find the final IC s of the sentence as 'the', 'player', 'plays', 'football'.             It can be shown through a tree diagram : 

                    Another sentence, 'The bird eats the apple' can be shown by a tree diagram with PS or phrase structure rules . The tree structure will be generated by six rules.  

1. S - NP - VP. 2. NP - DET - N. 3. VP - V - NP. 4. V - eats. 5. DET - the. 6. N - Bird, apple (NOTE: S= sentence, NP= noun phrase, VP= verb phrase, V= verb, N= noun, DET= determiner.                         IC analysis does not provide enough information of sentences and sentence construction. It can't identify a part of one sentence with another. Eventually, it doesn't let us know how to construct a sentence. It's the limitation of IC analysis . 

IC Analysis:

I = immediate.

C = Constituents.

IC Analysis = Immediate constituent analysis .

IC analysis is the process of analyzing units, such as word, phrase, clause or sentence which form a language. In another way, IC analysis is the analysis of a sentence to its immediate units or constituents . In this process, a sentence is broken into morphemes and analyzed. A sentence is first segmented into two and these two parts are also segmented into two and it continues until the smallest meaningful units are found. When there is difficulty in segmenting a sentence into two, then three or more is permitted.

IC analysis - Limitation :

IC analysis doesn’t trace which elements the constituent parts are. It doesn’t give much knowledge about sentence construction. IC analysis can’t indicate noun phrases are built on nouns and other phrases as the same.

Furthermore, IC analysis helps us to avoid ambiguity by understanding language units.

IC analysis examples:

  • The || Cat | run ||| s || into ||| the ||| room.
  • The || baby | bite ||| s || her ||| mom.
  • The || scholar | become ||| s || mad.
  • She | cry||| s || for ||| a ||| lollypop.
  • We | pray || forgiveness ||| to ||| Allah.
  • A || modest ||| person | hate ||| s || wine.

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Limitations of IC Analysis

Profile image of Saranya .S

In linguistics, immediate constituent analysis or IC analysis is a method of sentence analysis that was first mentioned by Leonard Bloomfield and developed further by Rulon Wells. This presentation provides you with the major shortcomings of the IC analysis that caused the further development of other modern grammatical models.

Related Papers

IJSRP Journal

This paper gives a concise study of I.C. Analysis. Moreover, this is a method of sentence analysis which was first mentioned by Leonard Bloomfield and developed further by Rulon Wells. As we know, the practice of I.C. Analysis is now widespread. This paper starts with a brief introduction of I.C. Analysis and explores what is I.C. Analysis. It also gives illustration of how sentences are analyzed and divided into constituents in the large construction. Then it discusses what is ICA, and its approach and frame. In this paper we also know how to analyze the ICA and how we analyze ICA sentences and it also discusses what are the limitations of ICA.

the ic analysis

Ruochen Niu

This contribution introduces a novel unit of syntactic analysis, which is called the component. The validity and utility of the component unit are established in terms of chunking. When informants organize the words of sentences into groups, they are creating chunks, and these chunks then qualify as components in dependency syntax. By acknowledging the nature of chunking and the component unit, it is possible to cast light on controversial aspects of dependency hierarchies. In particular, the component unit, informant data, and the reasoning based on these provide an argument in favor of the traditional DG assumptions about hierarchical status of many function words (auxiliary verbs, prepositions, subordinators, etc.), and in so doing, they contradict the Universal Dependencies (UD) annotation scheme. The data discussed here are from English, but the methodology and reasoning employed are easily extendable to other languages.

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Thermo Fisher (TMO) Introduces Dionex Inuvion IC system

Thermo Fisher Scientific, Inc. TMO recently introduced a new instrument for Ion chromatography to enhance labs' functional adaptability, efficiency, and reliability -- Thermo Scientific Dionex Inuvion Ion Chromatography (IC) system. The new system will make ion analysis simpler and more intuitive for labs of all sizes.

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More on New Launch

The Dionex Inuvion system helps labs operate more effectively by providing simply adjustable workflows and a small footprint. The adaptable platform can be modified to fulfill present analytical requirements while also assisting IC capabilities in adapting to changing sample types and workflow requirements in the future more efficiently and cost-effectively.

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IC Analysis: Advantages and Limitations

the ic analysis

The process of IC analysis always ends when the most minor constituents are reached, which are often words. However, the analysis can also be extended into words to acknowledge how words are structured. Most tree structures employed to represent the syntactic structure of sentences are products of some form of IC analysis.

Advantages of IC Analysis

Identification of the Layers of Relationship in a Construction

IC analysis helps to display the layers or units in a sentence graphically and how the units are hierarchically organised to form the sentence. It discovers the layers of relationships in a construction. English syntax is based on this ability of structures to function within larger structures, which serve other functions in still larger, more complex structures (sentences). Composing a more complex sentence, such as  The pretty girl put on her red and blue coat, kissed her mother, and left , demonstrates the nature of the relationship that must be negotiated if a hearer or a reader is to understand such a sentence. Anyone capable of understanding the sentence’s meaning has the mental capacity to keep all those relationships afloat as he hears or reads the sentence.

Fixity of Word Order

In IC analysis, the word order is not disturbed in any way. This advantage is best demonstrated by sorting the relationship found in the following sentences, which are composed of the same words but are different in word order:

  • The boy played marbles on his knees.
  • The boy on his knees played marbles.
  • On his knees, the boy played marbles.

These sentences are stylistically different. In the first, the prepositional phrase ‘on his knees’ modifies the verb phrase; in the second, it modifies the noun phrase; in the third, it modifies all the rest of the sentence. Yet, in the word order within the structure, ‘on his knees’ does not change.

To Account for Ambiguities and Distinguish Them

A famous example of  old men and women  can be paraphrased in two ways; it is either  old men and women of all ages  or  old men and women . The principle of expansion here allows us two interpretations. Either  old men  is an expansion of a single morpheme (E.g., men or boy) or ‘men and women’ is an expansion of a single morpheme (E.g., people or men).

  • old (men and women)
  • (old men) and women

Limitations of IC Analysis

Presumptions About the Grammatical Status of the Elements

Although IC analysis is supposed to precede any attempt to identify and classify the ICs as subjects, objects, or noun phrases, it is based on tacit assumptions about the grammatical status of the elements.

E.g., want to go

  • want to/go.

If we compare it with ‘want food’, the first analysis would clearly be ‘want to/go’. But the answer favoured ‘want/to go’ because the possibility of ‘to go’ is easy where obviously ‘to go’ is a constituent.

Here, such identification is grammatical because we accept an analysis which allows us to consider ‘to go’ as some nominal element and favouring the comparison with ‘want food’, so that ‘to go’ is an expansion of ‘food’ because it is of the same grammatical type.

Discontinuity

IC-analysis cannot assign a natural P-marker to sentences containing discontinuous constituents. That is, sometimes IC analysis cannot divide a construction into two because elements that belong together are separated in the sequence (i.e., discontinuous).

E.g., Is John coming?

Here, ‘is’ is nearer to coming than to John. Obviously, the ICs of this sentence are not ‘is’ and ‘John coming’, but rather ‘is…coming’ and ‘John’. There is no non-ad-hoc way of representing this diagrammatically.

Of course, we can always carry on the IC analysis by merely permitting discontinuity. However, this makes the very assumption on which IC analysis is based less plausible – that language is essentially a one-dimensional linear string that can be chopped up into decreasing segments. It must be recalled that IC analysis depends on expansion, the substitution of sequences by single morphemes, but discontinuous sentences are not sequences.

Lexical, Constructional or Derivational Ambiguities

Lexical ambiguity arises from the same word having more than one meaning

  • bank of the river
  • institution where we deposit money

Constructional ambiguity is due to the difference in layering.

E.g., The pen on the table that belongs to me.

Here, the problem is that one gets confused whether it is the table or the pen that  belongs to me .

Derivational ambiguity arises from the same constituents functioning differently.

E.g., the love of God.

It may mean God’s love for someone or someone’s love for God.

Constructional and derivational ambiguity can together be called structural homonymy. IC analysis can disambiguate specific constructions.

E.g., A Russian history teacher

  • A Russian history/teacher – teacher who teaches Russian history
  • A Russian/history teacher – the history teacher who is Russian

Syntactic ambiguity may be defined as follows: a sentence is syntactically ambiguous if it has two (or more) meanings that cannot be ascribed to the semantic structure of the words it is made up of. It is sometimes referred to in the literature as constructional homonymy. IC Analysis cannot account for constructional homonymy.

E.g., John washed the car in the garage.

It may mean that (a) the car was washed by John in the garage or (b) the car in the garage was washed by John.

IC Analysis is Not Below the Words

IC analysis assumes that there will be no division into pieces smaller than words (morphemes) until all the words have been divided. If we cut ‘criminal lawyer’ into ‘criminal/lawyer’, it does not sound tenable in actual practice because ‘criminal lawyer’ generally means a lawyer who deals with criminal cases. So unless we cut ‘criminal lawyer’ in a way like ‘criminal – lawyer’, the meaning does not come out clearly. But because IC analysis does not go below the level of words, we cannot analyse the phrase ‘criminal lawyer’ meaningfully.

Unbalanced Bracketing

IC analysis does not refer to our grammatical knowledge, so it only takes us a little further. Without the help of labelled bracketing, we cannot point out the sources of ambiguity in many sentences. The labelled bracketing can be used to differentiate the two possibilities, which is an example that is often against IC analysis.

E.g., What disturbed John was being disregarded by his friends.

The sentence has two possible interpretations:

  • which means John’s friends disregarded him, which disturbed him.
  • which means John’s friends were disregarding what disturbed him.

The Problem of Embedding

IC Analysis cannot account for sentences involving embedding.

E.g., The boy who won the prize is my cousin.

The Problem of Conjoining

IC Analysis cannot handle conjoining.

E.g., I will go and meet him.

The Problem of Unstated Elements

IC analysis fails to show elements that are unstated in a sentence.

E.g., hit the ball

The element ‘you’ is missing here. There is no way of showing this in the IC analysis.

The Problem of the Relationship Between Sentence Types

IC analysis fails to show the relationship between sentence types such as active and passive, affirmative and negative, and statement and question.

E.g., Kapil hit a six. A six was hit by Kapil.

Here, one is active, and the other is passive, a relation not visible in IC analysis.

The Problem of Overlapping ICs

Many times, overlapping ICs also cause a problem.

E.g., He has no interest in, or taste for, music.

The sentence means to convey that he has no interest in music; he has no taste for music. The word ‘no’ applies to both interest and taste. It is not possible to show this in the IC analysis.

The Problem of Structural Similarity and Different Grammatical Relations

Some sentences are structurally similar but semantically different.

E.g., John is easy to please. John is eager to please.

IC analysis cannot explain such sentences unless they are broken up into simple pairs of sentences. In this case, we may have the following groups.

  • (It) is easy. Someone pleases John.
  • John is eager. He wants to please.

The process and the result of IC analysis can, however, vary greatly based upon whether one chooses the constituency relation of phrase structure grammars (= constituency grammars) or the dependency relation of dependency grammars as the underlying principle that organises constituents into hierarchical structures. An important aspect of IC analysis in phrase structure grammar is that each word is a constituent by definition. The process is, however, much different in dependency grammars since many individual words do not end up as constituents in dependency grammars.

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I do agree with all the ideas you’ve introduced in your post. They’re really convincing and can definitely work.

Still, the posts are very quick for beginners. Could you please extend them a bit from subsequent time? Thank you for the post.

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  1. Immediate constituent analysis

    In linguistics, immediate constituent analysis or IC analysis is a method of sentence analysis that was proposed by Wilhelm Wundt and named by Leonard Bloomfield.

  2. immediate constituent analysis

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  3. Constituent: Definition and Examples in Grammar

    One method of analyzing sentences, commonly known as immediate constituent analysis (or IC analysis), was introduced by the American linguist Leonard Bloomfield. As Bloomfield identified it, IC analysis involves breaking a sentence down into its parts and illustrating it with brackets or a tree diagram.

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  13. How is bracketing used in IC analysis?

    1 Answer. Sorted by: 1. The phrase (IC) "what happened next" is the subject of the sentence. The phrase "all present to the meeting" is the (direct) object. Thus, the basic IC structure is. [ [what happened next] [VP [astonished] [all present at the meeting]]] The object NP can be further analyzed as. [all [AP present [PP at [NP the meeting]]]]

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  18. IC analysis through tree diagram

    IC analysis is a technique of analyzing constituents of a sentence. It can be segmented from a phrase, clause or sentence. In IC analysis, a sentence is broken up into immediate constituents. Divided constituents are also divided into further immediate constituents. This process continues until a meaningful unit of a word or a word is gotten.

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  22. (PPT) Limitations of IC Analysis

    Saranya .S. 2020. In linguistics, immediate constituent analysis or IC analysis is a method of sentence analysis that was first mentioned by Leonard Bloomfield and developed further by Rulon Wells. This presentation provides you with the major shortcomings of the IC analysis that caused the further development of other modern grammatical models.

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  26. IC Analysis: Advantages and Limitations

    An important aspect of IC analysis in phrase structure grammar is that each word is a constituent by definition. The process is, however, much different in dependency grammars since many individual words do not end up as constituents in dependency grammars. δάσκαλος (dáskalos) means the teacher in Greek.