Towards fine-grained citation function classification

Xiang Li, Yifan He, Adam Meyers, Ralph Grishman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We look into the problem of recognizing citation functions in scientific literature, trying to reveal authors' rationale for citing a particular article. We introduce an annotation scheme to annotate citation functions in scientific papers with coarse-to-fine-grained categories, where the coarse-grained annotation roughly corresponds to citation sentiment and the finegrained annotation reveals more about citation functions. We implement a Maximum Entropy-based system trained on annotated data under this scheme to automatically classify citation functions in scientific literature. Using combined lexical and syntactic features, our system achieves the F-measure of 67%.

Original languageEnglish (US)
Title of host publicationInternational Conference Recent Advances in Natural Language Processing, RANLP
Pages402-407
Number of pages6
StatePublished - 2013
Event9th International Conference on Recent Advances in Natural Language Processing, RANLP 2013 - Hissar, Bulgaria
Duration: Sep 9 2013Sep 11 2013

Other

Other9th International Conference on Recent Advances in Natural Language Processing, RANLP 2013
CountryBulgaria
CityHissar
Period9/9/139/11/13

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software
  • Electrical and Electronic Engineering

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  • Cite this

    Li, X., He, Y., Meyers, A., & Grishman, R. (2013). Towards fine-grained citation function classification. In International Conference Recent Advances in Natural Language Processing, RANLP (pp. 402-407)