Identifying constant and unique relations by using time-series text

  • Authors:

  • Yohei Takaku;Nobuhiro Kaji;Naoki Yoshinaga;Masashi Toyoda

  • Affiliations:

  • Toyo Keizai Inc., Tokyo, Japan;University of Tokyo, Tokyo, Japan;University of Tokyo, Tokyo, Japan;University of Tokyo, Tokyo, Japan

  • Venue:
  • EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
  • Year:
  • 2012

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Abstract

Because the real world evolves over time, numerous relations between entities written in presently available texts are already obsolete or will potentially evolve in the future. This study aims at resolving the intricacy in consistently compiling relations extracted from text, and presents a method for identifying constancy and uniqueness of the relations in the context of supervised learning. We exploit massive time-series web texts to induce features on the basis of time-series frequency and linguistic cues. Experimental results confirmed that the time-series frequency distributions contributed much to the recall of constancy identification and the precision of the uniqueness identification.