Using entity features to classify implicit discourse relations
نویسندگان
چکیده
We report results on predicting the sense of implicit discourse relations between adjacent sentences in text. Our investigation concentrates on the association between discourse relations and properties of the referring expressions that appear in the related sentences. The properties of interest include coreference information, grammatical role, information status and syntactic form of referring expressions. Predicting the sense of implicit discourse relations based on these features is considerably better than a random baseline and several of the most discriminative features conform with linguistic intuitions. However, these features do not perform as well as lexical features traditionally used for sense prediction. Disciplines Computer Sciences Comments Louis, A., Joshi, A., Prasad, R., & Nenkova, A., Using Entity Features to Classify Implicit Discourse Relations, The 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Sept. 2010, doi: anthology/ W10-4310 This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/712
منابع مشابه
Role Semantics for Better Models of Implicit Discourse Relations
Predicting the structure of a discourse is challenging because relations between discourse segments are often implicit and thus hard to distinguish computationally. I extend previous work to classify implicit discourse relations by introducing a novel set of features on the level of semantic roles. My results demonstrate that such features are helpful, yielding results competitive with other fe...
متن کاملInferring Implicit Causal Relationships in Biomedical Literature
Biomedical relations are often expressed between entities occurring within the same sentence through syntactic means. However, a significant portion of such relations (in particular, causal relations) are expressed implicitly across sentence boundaries. Inferring these discourse-level relations can be challenging in the absence of syntactic clues. In this paper, we present a study of textual ch...
متن کاملLeveraging Hierarchical Deep Semantics to Classify Implicit Discourse Relations via Mutual Learning Method
This paper presents a mutual learning method using hierarchical deep semantics for the classification of implicit discourse relations in English. With the absence of explicit discourse markers, traditional discourse techniques mainly concentrate on discrete linguistic features in this task, which always leads to data sparse problem. To relieve this problem, we propose a mutual learning neural m...
متن کاملLinguistic Properties Matter for Implicit Discourse Relation Recognition: Combining Semantic Interaction, Topic Continuity and Attribution
Modern solutions for implicit discourse relation recognition largely build universal models to classify all of the different types of discourse relations. In contrast to such learning models, we build our model from first principles, analyzing the linguistic properties of the individual top-level Penn Discourse Treebank (PDTB) styled implicit discourse relations: Comparison, Contingency and Exp...
متن کاملAutomatic sense prediction for implicit discourse relations in text
We present a series of experiments on automatically identifying the sense of implicit discourse relations, i.e. relations that are not marked with a discourse connective such as “but” or “because”. We work with a corpus of implicit relations present in newspaper text and report results on a test set that is representative of the naturally occurring distribution of senses. We use several linguis...
متن کامل