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

Parikshit Sondhi; Chengxiang Zhai

doi:10.1137/1.9781611973440.41

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

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	SIAM International Conference on Data Mining 2014, SDM 2014
Editors	Mohammed J. Zaki, Arindam Banerjee, Srinivasan Parthasarathy, Pang Ning-Tan, Zoran Obradovic, Chandrika Kamath
Publisher	Society for Industrial and Applied Mathematics Publications
Pages	361-369
Number of pages	9
ISBN (Electronic)	9781510811515
DOIs	https://doi.org/10.1137/1.9781611973440.41
State	Published - Jan 1 2014
Event	14th SIAM International Conference on Data Mining, SDM 2014 - Philadelphia, United States Duration: Apr 24 2014 → Apr 26 2014

Publication series

Name	SIAM International Conference on Data Mining 2014, SDM 2014
Volume	1

Other

Other	14th SIAM International Conference on Data Mining, SDM 2014
Country	United States
City	Philadelphia
Period	4/24/14 → 4/26/14

ASJC Scopus subject areas

Computer Science Applications
Software

Access to Document

10.1137/1.9781611973440.41

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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

A constrained hidden Markov model approach for non-explicit citation context extraction. / Sondhi, Parikshit; Zhai, Chengxiang.

SIAM International Conference on Data Mining 2014, SDM 2014. ed. / Mohammed J. Zaki; Arindam Banerjee; Srinivasan Parthasarathy; Pang Ning-Tan; Zoran Obradovic; Chandrika Kamath. Society for Industrial and Applied Mathematics Publications, 2014. p. 361-369 (SIAM International Conference on Data Mining 2014, SDM 2014; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sondhi, P & Zhai, C 2014, A constrained hidden Markov model approach for non-explicit citation context extraction. in MJ Zaki, A Banerjee, S Parthasarathy, P Ning-Tan, Z Obradovic & C Kamath (eds), SIAM International Conference on Data Mining 2014, SDM 2014. SIAM International Conference on Data Mining 2014, SDM 2014, vol. 1, Society for Industrial and Applied Mathematics Publications, pp. 361-369, 14th SIAM International Conference on Data Mining, SDM 2014, Philadelphia, United States, 4/24/14. https://doi.org/10.1137/1.9781611973440.41

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

Sondhi, Parikshit ; Zhai, Chengxiang. / A constrained hidden Markov model approach for non-explicit citation context extraction. SIAM International Conference on Data Mining 2014, SDM 2014. editor / Mohammed J. Zaki ; Arindam Banerjee ; Srinivasan Parthasarathy ; Pang Ning-Tan ; Zoran Obradovic ; Chandrika Kamath. Society for Industrial and Applied Mathematics Publications, 2014. pp. 361-369 (SIAM International Conference on Data Mining 2014, SDM 2014).

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