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An iterative approach for the global estimation of sentence similarity

Author

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  • Tomoyuki Kajiwara
  • Danushka Bollegala
  • Yuichi Yoshida
  • Ken-ichi Kawarabayashi

Abstract

Measuring the similarity between two sentences is often difficult due to their small lexical overlap. Instead of focusing on the sets of features in two given sentences between which we must measure similarity, we propose a sentence similarity method that considers two types of constraints that must be satisfied by all pairs of sentences in a given corpus. Namely, (a) if two sentences share many features in common, then it is likely that the remaining features in each sentence are also related, and (b) if two sentences contain many related features, then those two sentences are themselves similar. The two constraints are utilized in an iterative bootstrapping procedure that simultaneously updates both word and sentence similarity scores. Experimental results on SemEval 2015 Task 2 dataset show that the proposed iterative approach for measuring sentence semantic similarity is significantly better than the non-iterative counterparts.

Suggested Citation

  • Tomoyuki Kajiwara & Danushka Bollegala & Yuichi Yoshida & Ken-ichi Kawarabayashi, 2017. "An iterative approach for the global estimation of sentence similarity," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-15, September.
    • Handle: RePEc:plo:pone00:0180885
    DOI: 10.1371/journal.pone.0180885
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    Cited by:

    1. Alicia Lara-Clares & Juan J Lastra-Díaz & Ana Garcia-Serrano, 2021. "Protocol for a reproducible experimental survey on biomedical sentence similarity," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-28, March.

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