Minimally Supervised Question Classification and Answering based on WordNet and Wikipedia

Joseph Chang, Tzu Hsi Yen, Richard Tzong Han Tsai

Research output: Contribution to conferencePaperpeer-review

Abstract

In this paper, we introduce an automatic method for classifying a given question using broad semantic categories in an existing lexical database (i.e., WordNet) as the class tagset. For this, we also constructed a large scale entity supersense database that contains over 1.5 million entities to the 25 WordNet lexicographer’s files (supersenses) from titles of Wikipedia entry. To show the usefulness of our work, we implement a simple redundancy-based system that takes the advantage of the large scale semantic database to perform question classification and named entity classification for open domain question answering. Experimental results show that the proposed method outperform the baseline of not using question classification.

Original languageEnglish
StatePublished - 2009
Event21st Conference on Computational Linguistics and Speech Processing, ROCLING 2009 - Taichung, Taiwan
Duration: 1 Sep 20092 Sep 2009

Conference

Conference21st Conference on Computational Linguistics and Speech Processing, ROCLING 2009
Country/TerritoryTaiwan
CityTaichung
Period1/09/092/09/09

Keywords

  • Question answering
  • Question classification
  • Semantic category
  • Wikipedia
  • WordNet

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