Improving scientific relation classification with task specific supersense

Qin Dai, Naoya Inoue, Paul Reisert, Kentaro Inui

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

Classifying the relationship between entitiesis an important natural language processing(NLP) task. Scientific Relation Classificationaims at automatically categorizing scientificsemantic relationships among entities in scientific documents. Conventionally, only taskunspecific supersense, such as supersense (orhyernym) from WordNet (e.g., ANIMAL is thesupersense of "dog"), is used as a feature for relation classification. In this work, we hypothesize that task specific supersense could also beutilized as an informative feature for relationclassification. Specifically, we define a newentity type based on the property of a giventask, and facilitate scientific relation classification with the task specific supersense. Ourexperiments on three different datasets provethe effectiveness of the task specific supersenseon relation classification in scientific articles.

Original languageEnglish
Pages129-138
Number of pages10
Publication statusPublished - 2018
Event32nd Pacific Asia Conference on Language, Information and Computation, PACLIC 2018 - Hong Kong, Hong Kong
Duration: 2018 Dec 12018 Dec 3

Conference

Conference32nd Pacific Asia Conference on Language, Information and Computation, PACLIC 2018
CountryHong Kong
CityHong Kong
Period18/12/118/12/3

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science (miscellaneous)

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