Recognizing implicit discourse relations through abductive reasoning with large-scale lexical knowledge

Jun Sugiura, Naoya Inoue, Kentaro Inui

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

Discourse relation recognition is the task of identifying the semantic relationships between textual units. Conventional approaches to discourse relation recognition exploit surface information and syntactic information as machine learning features. However, the performance of these models is severely limited for implicit discourse relation recognition. In this paper, we propose an abductive theorem proving (ATP) approach for implicit discourse relation recognition. The contribution of this paper is that we give a detailed discussion of an ATP-based discourse relation recognition model with open-domain web texts.

Original languageEnglish
Pages (from-to)76-87
Number of pages12
JournalCEUR Workshop Proceedings
Volume1044
Publication statusPublished - 2013 Jan 1
Event1st Workshop on Natural Language Processing and Automated Reasoning, NLPAR 2013 - Co-located with 12th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2013 - A Corunna, Spain
Duration: 2013 Sep 15 → …

Keywords

  • Abductive reasoning
  • Association information
  • Discourse relation
  • Lexical knowledge

ASJC Scopus subject areas

  • Computer Science(all)

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