DT-Neg: Tutorial Dialogues Annotated for Negation Scope and Focus in Context
15014Authors
nan
Abstract
AbstractNegation is often found more frequent in dialogue than commonly written texts, such as literary texts. Furthermore, the scope and focus of negation depends on context in dialogues than other forms of texts. Existing negation datasets have focused on non-dialogue texts such as literary texts where the scope and focus of negation is normally present within the same sentence where the negation is located and therefore are not the most appropriate to inform the development of negation handling algorithms for dialogue-based systems. In this paper, we present DT -Neg corpus (DeepTutor Negation corpus) which contains texts extracted from tutorial dialogues where students interacted with an Intelligent Tutoring System (ITS) to solve conceptual physics problems. The DT -Neg corpus contains annotated negations in student responses with scope and focus marked based on the context of the dialogue. Our dataset contains 1,088 instances and is available for research purposes at http://language.memphis.edu/dt-neg.
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DT-Neg: Tutorial Dialogues Annotated for Negation Scope and Focus in Context Rajendra Banjade, Vasile Rus Department of Computer Science, Institute for Intelligent Systems The University of Memphis, Memphis, TN, USA {rbanjade, vrus}@memphis.edu Abstract Negation is often found more frequent in dialogue than commonly written texts, such as literary texts. Furthermore, the scope and focus of negation depends on context in dialogues than other forms of texts. Existing negation datasets have focused on non-dialogue texts such as literary texts where the scope and focus of negation is normally present within the same sentence where the negation is located and therefore are not the most appropriate to inform the development of negation handling algorithms for dialogue-based systems. In this paper, we present DT-Neg corpus (DeepTutor Negation corpus) which contains texts extracted from tutorial dialogues where students interacted with an Intelligent Tutoring System (ITS) to solve conceptual physics problems. The DT-Neg corpus contains annotated negations in student responses with scope and focus marked based on the context of the dialogue. Our dataset contains 1,088 instances and is available for research purposes at http://language.memphis.edu/dt-neg. Keywords: Negation, Cue, Scope, Focus, Dialogue, Corpus, Annotation 1. Introduction According to SIL International (Summer Institute of Linguistics), negation is a morphosyntactic operation in which a lexical item denies or inverts the meaning of another lexical item or construction. A negator (or negation cue), is a lexical item that expresses negation. Morphological negation is when a word is negated by an affix (prefix or suffix) as in un-happy or sense-less whereas in syntactic negation an entire clause is negated explicitly (using a negator) or implicitly, e.g. verbs or nominalizations that negate their complements such as fail or deny. In explicitly negated statements, negation is marked using cue words, such as not, no and neither nor. A negation cue word or negator can affect the meaning of a part of the sentence in which it appears or part of another sentence from the discourse context. The part of the sentence affected by the negation cue is called negation scope. The part of the scope that is most prominently or explicitly negated is called negation focus (Huddleston & Pullum, 2002). An example of negation is shown in the following sentence where we indicate the negation cue (in <>), the negation scope (in []) and the negation focus (in {}): The desk stops moving because [there is] <no> [{net force} acting on it]. Negation is a frequent and complex phenomenon in natural language. Tottie (1991) reports that negation is twice as frequent in spoken sentences (27.6 per 1,000 words) as in written text (12.8 per 1,000 words). Councill and Velikovich (2010) report that 19% of the product review sentences contain negations. The negation frequency and its key role in many applications such as intelligent tutoring, sentiment analysis, and information extraction, emphasize the importance of the negation handling problem. For instance, in order to understand student responses and provide appropriate feedback in Intelligent Tutoring Systems (ITS; Graesser et al. (2004); Dzikovska et al. (2010); …
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Labeled Text
AbstractNegation is often found more frequent in dialogue than commonly written texts, such as literary texts. Furthermore, the scope and focus of negation depends on context in dialogues than other forms of texts. Existing negation datasets have focused on non-dialogue texts such as literary texts where the scope and focus of negation is normally present within the same sentence where the negation is located and therefore are not the most appropriate to inform the development of negation handling algorithms for dialogue-based systems. In this paper, we present DT -Neg corpus (DeepTutor Negation corpus) which contains texts extracted from tutorial dialogues where students interacted with an Intelligent Tutoring System (ITS) to solve conceptual physics problems. The DT -Neg corpus contains annotated negations in student responses with scope and focus marked based on the context of the dialogue. Our dataset contains 1,088 instances and is available for research purposes at
Publication Details
- Paper ID:
- 15014
- Year:
- 2016
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan