Understanding how social power structures affect the way we interact with one another is of great interest to social scientists who want to answer fundamental questions about human behavior, as well as to computer scientists who want to build automatic methods to infer the social contexts of interactions. In this paper, we employ advancements in extra-propositional semantics extraction within NLP to study how author commitment reflects the social context of an interaction. Specifically, we investigate whether the level of commitment expressed by individuals in an organizational interaction reflects the hierarchical power structures they are part of. We find that subordinates use significantly more instances of non-commitment than superiors. More importantly, we also find that subordinates attribute propositions to other agents more often than superiors do --- an aspect that has not been studied before. Finally, we show that enriching lexical features with commitment labels captures important distinctions in social meanings.
Automatic hedge uncertainty detection is a very closely related task to belief detection. The belief tagging framework we use aims to capture the cognitive states of authors, whereas hedges are linguistic expressions that convey one of those cognitive states --- non-committed beliefs. Automatic hedge uncertainty detection has generated active research in recent years within the NLP community. Early work in this area focused on detecting speculative language in scientific text @cite_14 @cite_11 @cite_20 . The open evaluation as part of the CoNLL shared task in 2010 to detect uncertainty and hedging in biomedical and Wikipedia text @cite_32 triggered further research on this problem in the general domain @cite_31 @cite_5 @cite_24 @cite_3 . Most of this work was aimed at formal scientific text in English. More recent work has tried to extend this work to other genres @cite_21 @cite_37 and languages @cite_0 @cite_34 , as well as building general purpose hedge lexicons @cite_46 . In our work, we use the lexicons from @cite_46 to capture hedges in text.
@cite_37 @cite_14 @cite_46 @cite_21 @cite_32 @cite_3 @cite_24 @cite_0 @cite_5 @cite_31 @cite_34 @cite_20 @cite_11
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Objective: Hedging is frequently used in both the biological literature and clinical notes to denote uncertainty or speculation. It is important for text-mining applications to detect hedge cues and their scope; otherwise, uncertain events are incorrectly identified as factual events. However, due to the complexity of language, identifying hedge cues and their scope in a sentence is not a trivial task. Our objective was to develop an algorithm that would automatically detect hedge cues and their scope in biomedical literature. Methodology: We used conditional random fields (CRFs), a supervised machine-learning algorithm, to train models to detect hedge cue phrases and their scope in biomedical literature. The models were trained on the publicly available BioScope corpus. We evaluated the performance of the CRF models in identifying hedge cue phrases and their scope by calculating recall, precision and F1-score. We compared our models with three competitive baseline systems. Results: Our best CRF-based model performed statistically better than the baseline systems, achieving an F1-score of 88 and 86 in detecting hedge cue phrases and their scope in biological literature and an F1-score of 93 and 90 in detecting hedge cue phrases and their scope in clinical notes. Conclusions: Our approach is robust, as it can identify hedge cues and their scope in both biological and clinical text. To benefit text-mining applications, our system is publicly available as a Java API and as an online application at http: hedgescope.askhermes.org. To our knowledge, this is the first publicly available system to detect hedge cues and their scope in biomedical literature. We investigate the automatic detection of sentences containing linguistic hedges using corpus statistics and syntactic patterns. We take Wikipedia as an already annotated corpus using its tagged weasel words which mark sentences and phrases as non-factual. We evaluate the quality of Wikipedia as training data for hedge detection, as well as shallow linguistic features. Uncertainty detection is essential for many NLP applications. For instance, in information retrieval, it is of primary importance to distinguish among factual, negated and uncertain information. Current research on uncertainty detection has mostly focused on the English language, in contrast, here we present the first machine learning algorithm that aims at identifying linguistic markers of uncertainty in Hungarian texts from two domains: Wikipedia and news media. The system is based on sequence labeling and makes use of a rich feature set including orthographic, lexical, morphological, syntactic and semantic features as well. Having access to annotated data from two domains, we also focus on the domain specificities of uncertainty detection by comparing results obtained in indomain and cross-domain settings. Our results show that the domain of the text has significant influence on uncertainty detection. We present a preliminary pilot study of belief annotation and automatic tagging. Our objective is to explore semantic meaning beyond surface propositions. We aim to model people's cognitive states, namely their beliefs as expressed through linguistic means. We model the strength of their beliefs and their (the human) degree of commitment to their utterance. We explore only the perspective of the author of a text. We classify predicates into one of three possibilities: committed belief, non committed belief, or not applicable. We proceed to manually annotate data to that end, then we build a supervised framework to test the feasibility of automatically predicting these belief states. Even though the data is relatively small, we show that automatic prediction of a belief class is a feasible task. Using syntactic features, we are able to obtain significant improvements over a simple baseline of 23 F-measure absolute points. The best performing automatic tagging condition is where we use POS tag, word type feature AlphaNumeric, and shallow syntactic chunk information CHUNK. Our best overall performance is 53.97 F-measure.