A constrained hidden Markov model approach for non-explicit citation context extraction

Parikshit Sondhi, Chengxiang Zhai

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

Abstract

In this paper we present a constrained hidden markov model based approach for extracting non-explicit citing sentences in research articles. Our method involves first independently training a separate HMM for each citation in the article being processed and then performing a constrained joint inference to label non-explicit citing sentences. Results on a standard test collection show that our method significantly outperforms the baselines and is comparable to the state of the art approaches.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2014, SDM 2014
EditorsMohammed J. Zaki, Arindam Banerjee, Srinivasan Parthasarathy, Pang Ning-Tan, Zoran Obradovic, Chandrika Kamath
PublisherSociety for Industrial and Applied Mathematics Publications
Pages361-369
Number of pages9
ISBN (Electronic)9781510811515
DOIs
StatePublished - Jan 1 2014
Event14th SIAM International Conference on Data Mining, SDM 2014 - Philadelphia, United States
Duration: Apr 24 2014Apr 26 2014

Publication series

NameSIAM International Conference on Data Mining 2014, SDM 2014
Volume1

Other

Other14th SIAM International Conference on Data Mining, SDM 2014
CountryUnited States
CityPhiladelphia
Period4/24/144/26/14

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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  • Cite this

    Sondhi, P., & Zhai, C. (2014). A constrained hidden Markov model approach for non-explicit citation context extraction. In M. J. Zaki, A. Banerjee, S. Parthasarathy, P. Ning-Tan, Z. Obradovic, & C. Kamath (Eds.), SIAM International Conference on Data Mining 2014, SDM 2014 (pp. 361-369). (SIAM International Conference on Data Mining 2014, SDM 2014; Vol. 1). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611973440.41