Implicit discourse relation recognition by selecting typical training examples

Xun Wang, Sujian Li, Jiwei Li, Wenjie Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

27 Citations (Scopus)


Implicit discourse relation recognition is a challenging task in the natural language processing field, but important to many applications such as question answering, summarizat ion and so on. Previous research used either art ificially created implicit discourse relat ions with connectives removed from explicit relations or annotated implicit relat ions as training data to detect the possible implicit relations, and do not further discern which examples are fit to be training data. This paper is the first time to apply a different typical/atypical perspective to select the most suitable discourse relation examples as training data. To differentiate typical and atypical examples for each discourse relation, a novel single centroid clustering algorithm is proposed. With this typical/atypical distinction, we aim to recognize those easily identified discourse relations more precisely so as to promote the performance of the implicit relation recognition. The experimental results verify that the proposed new method outperforms the state -of-the-art methods.
Original languageEnglish
Title of host publication24th International Conference on Computational Linguistics - Proceedings of COLING 2012: Technical Papers
Number of pages16
Publication statusPublished - 1 Dec 2012
Event24th International Conference on Computational Linguistics, COLING 2012 - Mumbai, India
Duration: 8 Dec 201215 Dec 2012


Conference24th International Conference on Computational Linguistics, COLING 2012


  • Discourse relation recognition
  • Implicit discourse relation
  • Single centroid clustering

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Language and Linguistics
  • Linguistics and Language


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