This chapter presents a theoretical framework and preliminary results for manual categorization of explicit certainty information in 32 English newspaper articles. Our contribution is in a proposed categorization model and analytical framework for certainty identification. Certainty is presented as a type of subjective information available in texts. Statements with explicit certainty markers were identified and categorized according to four hypothesized dimensions -level, perspective, focus, and time of certainty. The preliminary results reveal an overall promising picture of the presence of certainty information in texts, and establish its susceptibility to manual identification within the proposed four-dimensional certainty categorization analytical framework. Our findings are that the editorial sample group had a significantly higher frequency of markers per sentence than did the sample group of news stories. For editorials, high level of certainty, writer's point of view, and future and present time were the most populated categories. For news stories, the most common were high and moderate levels, directly involved third party's point of view, and past time. These patterns have positive practical implications for automation. Keywords: certainty, certainty identification, certainty categorization model, subjectivity, manual tagging, natural language processing, linguistics, information extraction, information retrieval; uncertainty, doubt, epistemic comments, evidentials, hedges, hedging, certainty expressions; levels of certainty, point of view, annotating opinions; newspaper article analysis, analysis of editorials. 1 Analytical Framework The fields of Information Extraction (IE) and Natural Language Processing (NLP) have not yet addressed the task of certainty identification. It presents an ongoing theoretical and implementation challenge. Even though the linguistics literature has abundant intellectual investigations of closely related concepts, it has not yet provided NLP with a holistic certainty identification approach that would include clear definitions, theoretical underpinnings, validated analysis results, and a vision for practical applications. Unravelling the potential and demonstrating the usefulness of certainty analysis in an information-seeking situation is the driving force behind this preliminary research effort. Certainty identification is defined here as an automated process of extracting information from certainty-qualified texts or individual statements along four hypothesized dimensions of certainty, namely: • what degree of certainty is indicated (LEVEL), • whose certainty is involved (PERSPECTIVE), • what the object of certainty is (FOCUS), and • what time the certainty is expressed (TIME). Some writers consciously strive to produce a particular effect of certainty due to training or overt instructions. Others may do it inadvertently. A writer's certainty level may remain constant in a text and be unnoticed by the reader, or it may fluctuate from statement to statement and blatantly attract readers' attention. There may be evident traces of such writers' behavior that may become apparent upon a closer examination with a systematic theoretical framework. The difficulty is to discern such traces at the discourse, syntactic, semantic, and lexical levels, wherever such explicit information is available and to be able to recognize these explicit markers with a series of NLP algorithms. The importance of assessing how certain writers are about their statements is evident, especially in the stream of constantly updated news reports. Readers want to know, for instance, how sure writers or experts might be about public policy changes, about a possibility of a political or a financial turmoil, about what the government's intentions are regarding interest rates or about chances of coup d'etats versus peaceful transfers of power. Recognizing such certainty assessments would traditionally be considered a task for humans. While humans may rely to some extent on the big picture as obtained from world knowledge and prior experience, much certainty information comes from linguistic coding in texts and may be accessible to a systematic analysis with the help of NLP algorithms. Combined with the capabilities of an IE system, the task of linguistic de-coding of certainty information could then be handled successfully automatically, and the results could be presented to users for confirmation and possible modifications. A typical dictionary definition of certainty is "the quality or state of mind of being free from doubt, especially on the basis of evidence" (Merriam-Webster 2004) . The notion of certainty in the context of this chapter incorporates a full spectrum of certainty states ranging from doubt to complete conviction in the truth of a statement. There are several related concepts that have been previously addressed in NLP and linguistics literature: subjectivity, modality, evidentiality, and hedging. This section reports on how these closely related linguistic concepts are interpreted to define certainty, and concludes with a list of terms that are considered to be explicit certainty markers. This study departs from the notion of subjectivity. Certainty is a type of subjectivity identifiable in texts. Subjectivity has been defined in NLP as "aspects of language used to express opinions and evaluations" (Wiebe 1994 , Wiebe et al. 2001 . Cognitive Grammar describes subjectivity as "a part of the conceptual structure of information that lies behind linguistic 'packaging'" (Mushin 2001). Uncertainty, or certainty in terms of this chapter, is the speculative type of subjectivity (Wiebe 2000) that is analogous to the other types of subjectivity for which manual and automated tagging has proven to be a feasible NLP task (Wiebe et al. 2001 ). Subjectivity tagging is considered particularly relevant for the news report genre (Wiebe et al. 2001) . When developing news report schemata components for an automated text structurer, Liddy et al. (1993) noted that subjectivity, or objectivity, as an attribute in texts, deserved special attention. They observed that binary distinctions of statements (e.g., '+ subjective' or '-subjective') may not be sufficient to adequately represent micro-level similarities and distinctions in texts. In addition, discourse components may have multiple dimensions embedded in each of the concept labels (Liddy et al. 1995) . This study further explores dimensions of certainty in written news reports and editorials. Certainty can also be seen as a variety of epistemic modality expressed through epistemic comments. One type of epistemic comment is certainty expressions (e.g., probably, perhaps, undoubtedly) that provide clues to the writer's certainty or assessment of the truth of a statement and qualify a writer's attitude towards expressed knowledge. Epistemic comments reflect epistemic modality, which is described in Functional Linguistics as a writer's assumptions or assessments of possibilities expressed in statements, specifically regarding confidence in the truth of the expressed propositions (Coates 1983). Writer's confidence in the truth is synonymous with certainty. In other words, certainty is a writer's assessment of the truth of the statement. Certainty, in particular in languages other than English, can be expressed by means of evidentials that reveal a degree of reliability of expressed information. English resorts to other reportive means, such as attributive adverbials (e.g., supposedly, allegedly) and reporting verbs (e.g., claim, suggest). Evidentials were originally narrowly described as "suffixes expressing subjective relations… those expressing subjective knowledge" (Mushin 2001) and later understood as a semantic category that specifies type of source of information. Based on her comparison of Macedonian, Japanese and English corpora, Mushin (2001) concluded that English lacks clear grammatical markers of evidentiality, and that most types of English discourse are "faceless" in the sense of lacking epistemic evaluation in a grammatical inventory of reportive suffixes or other purely grammatical manifestations. However, she comes to a promising conclusion that English compensates for such lack of reportive means by other identifiable means by which speakers express, for instance, that the story they are telling was the product of someone else's telling. In particular, she notes that English does have a rich inventory of adverbials of "propositional attitude" (Mushin 2001). Choice of reporting verbs depends on how strongly the writer wants to be aligned with the reported source (Hyland 1998). Bergler et al. (2004) also hypothesize that the description of the source and the choice of the attributive or reporting verbs can in fact express the writer's level of confidence in the attributed material. Such verbs can be used "both, to bolster a claim made in the text already, or to distance the author from the attributed material, implicitly lowering its credibility" (Anick & Bergler, 1992 , cited in Bergler et al., 2004. In the same line of evidential semantics research, Chafe (1986) suggested a model that addresses reliability of knowledge expressed through evidentials. Knowledge was broadly defined as "the basic information whose status is qualified in one way or another by markers of evidentiality", where the notion of evidentiality extends beyond evidence and can be as inclusive as any "attitude toward knowledge" (Chafe 1986). In other words, he suggests different statuses that reveal reliability of expressed information: "People are aware, though not necessarily consciously aware, that some things they know are surer bets for being true than others, that not all knowledge is equally reliable." This is what is called "certainty" in this chapter. Chafe continues: "Thus, one way in which knowledge may be qualified is with an expression indicating the speaker's assessment of its degree of reliability, the likelihood of its being a fact." Chafe's degrees of reliability can be expressed in English through propositional attitude adverbials (in Mushin's terms), which is the same as epistemic comments (in Coates' terms), or explicit certainty markers in this chapter's terms. The term traditionally associated with linguistic uncertainty, especially in scientific writing, is hedging. Hedging was introduced by Lackoff (1972) and has generally been defined as "words whose job is to make things more or less fuzzy". In Hyland (1998) hedging refers to "any linguistic means used to indicate either a) a lack of commitment to the truth value of an accompanying proposition, or b) a desire not to express that commitment categorically." In research articles, hedges are "a crucial means of presenting new claims for ratification and are among the primary features which shape the research article as the principle vehicle for new knowledge" (Hyland 1998). Hyland (1998) identifies several categories of the way hedges can be expressed in everyday speech and scientific writing. The following surface lexical markers are used to attenuate strength of utterance: epistemic adjectives, epistemic adverbs, lexical verbs, auxiliary verbs, prosody, tag questions, and verbal fillers. Syntactic markers include if-clauses of condition and concessions, contrastive markers (e.g., nevertheless), and passivization (e.g., it can be questioned). Several other devices are classified as hedges particular to scientific writing only: hedging quantities for purposeful imprecision, admitting to lack of knowledge, citing a source, and referring to limitations (of the model, experimental conditions, or methods) (Hyland 1998). Thus, hedging is a device that indicates lack of commitment to the statement, reveals scepticism, expresses caution, or displays an open mind about a proposition. In this study hedges classify statements into low or moderate levels of certainty. Several other linguistic means of a writer's assessment of knowledge such as shields, approximators (Lackoff 1972), understatements, and tentatives; as well as intensifiers (Cappon 2000), emphatics (Holmes 1990), boosters and assertives (Searle 1979 ) are considered to be explicit certainty markers of varying certainty levels. In summary, certainty is viewed as a type of subjective information available in texts and a form of epistemic modality expressed through explicitly-coded linguistic means. Such devices as subjectivity expressions, epistemic comments, evidentials, reporting verbs, attitudinal adverbials, hedges, shields, approximators, understatements, tentatives, intensifiers, emphatics, boosters, and assertives, often overlap in their definitions, classifications, and lexical representations in English. In essence, they perform the same role for the purpose of this study. They explicitly signal presence of certainty information that covers a full continuum of writer's confidence, ranging from uncertain possibility and withholding full commitment to statements to a confident necessity, reassurance, and emphasizing of the full commitment to statements. For the purpose of this study, these devices are all called explicit certainty markers. In the remainder of the paper, we develop a certainty categorization model, report on preliminary results, and conclude with outlined challenges and applications. Expressing some degree of certainty in language is inevitable, just as one is bound to have a spatial angle of vision. Certainty is generally understood to be a pragmatic position rather than a grammatical feature. Banfield (1982) observed that subjectivity, a closely related concept, is a spatial notion by nature, and in language, it is taken to be located in a speaker. While it is questionable whether truly objective statements may exist, it seems even less likely that a statement may exist without a degree of certainty in the presented information. Each statement should potentially reveal a particular pragmatic position, or a level of certainty, but not all of them are explicitly marked. The commonly used declarative mood of stating facts and opinions may have an implied certainty level without any explicit indication that would be considered identifiable for Information Extraction purposes. Statements with implicit certainty levels are not discussed under the current categorization model, they are grouped into a separate category of no identifiable explicit certainty information. The proposed certainty categorization model distinguishes 4 dimensions for explicit certainty. The certainty level is the first and most important dimension. The other three are perspective, focus, and time (Figure 1 ). Each dimension is subdivided into several categories which creates 72 possible dimension-category combinations (4 levels by 3 perspectives by 2 foci by 3 times).  The concept of certainty seems to fall inherently into levels. We suggest the division of the certainty level dimension into four categories -absolute, high, moderate, and low. The excerpts below exemplify decreasing degree of certainty from absolute certainty in the first example to low certainty in the last one. The explicit certainty markers are highlighted in bold. Having an explicit certainty marker that places a statement into one of the levels of certainty, is what distinguishes a certainty-qualified statement from a non-marked one. (1) Eventually, however, auditors will almost certainly have to form a tough selfregulatory body that can oversee its members' actions… (ID=e24.18) (2) … but clearly an opportunity is at hand for the rest of the world to pressure both sides to devise a lasting peace based on democratic values and respect for human rights.  The second dimension in Figure 1 , the certainty perspective, separates the certainty point of view into the writer's and the reported points of view. The writer's certainty refers to the experiencer of certainty at the time of writing a statement as exemplified below. (5) More evenhanded coverage of the presidential race would help enhance the legitimacy of the eventual winner, which now appears likely to be Putin. (ID=e8.14) The certainty is clearly attributed to the writer in the example above. A practical question is whether third parties' voices can be isolated from the author's since they are presented through the author's prism. Reported point of view can refer to either individuals or organizations. It is divided into two subcategories. First, those of directly involved third parties, such as victims, witnesses, and survivors, are direct event participants, who are either present at the described event or whose life is directly affected by the events. Second, those of indirectly involved third parties, are such as experts, authorities, and analysts, who are tangentially related to the event in professional or other capacities. (6) The Dutch recruited settlers with an advertisement that promised to provide them with slaves who "would accomplish more work for their masters, … (ID=e27.13) (7) The historian Ira Berlin, author of "Many Thousands Gone,'' estimates that one slave perished for every one who survived capture in the African interior… (ID=e27.8) In the first example the writer reports on the certainty of the group of direct participants, the Dutch; and in the second example, the writer cites another expert's opinion. The writer's certainty as expressed in text should not be confused with the reader's certainty that the text is believable. The writer's certainty about his or her own and others' assertions is captured in texts. The reader's certainty is related to the numerous factors which inform his or her own subjectivity, or point of view. The former is accessible for analysis since it has a written record, but the latter is less tangible and may reflect high inter-personal variability. Thus, the reader's certainty is out of scope for this study, as this inquiry focuses on the writer's certainty model and its multi-dimensional complexity in the newspaper context. The third dimension, the certainty focus, is divided into abstract and factual information in the narrative. The term focus is used in van Dijk's (1981) localized selection sense as the referent, or the object, subject, or topic of conversation that is being talked about, or predicated upon in a particular localized syntactic unit, such as a sentence or clause. Abstract information may include judgments, opinions, attitudes, beliefs, moral principles, and emotions. Usually such statements, as in the example below, reflect an idea that does not represent an external reality, but rather a hypothesized world, existing only in someone's mind, and separated from embodiment or object of nature. (8) In Iraq, the first steps must be taken to put a hard-won new security council resolution on arms inspections into effect. (ID=e8.12)
Another work proposes a manual categorization of newspaper information adopting a certainty model of level(absolute, higher, moderate and low), perspective (representing either the author's or a reported viewpoint, or alternatively the third party's viewpoint that is directly or indirectly involved in the event), focus (abstract or factual information-based) and time (past, present, or future) REF .
Certainty identification in texts: Categorization model and manual tagging results
Computing Attitude and Affect in Text: Theory and Applications
Computing Attitude and Affect in Text: Theory and Applications
Computer Science