Topic segmentation of news speech using word similarity

  • Authors:
  • Seiichi Takao;Jun Ogata;Yasuo Ariki

  • Affiliations:
  • -;-;-

  • Venue:
  • MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
  • Year:
  • 2000

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Abstract

Conventional topic segmentation utilizes cosine measure as the similarity between consecutive passages. However, the cosine measure has a problem that it can not reflect the similarity unless exactly the same words are included in the passages. To solve this problem, in this paper, we propose a method to acquire the word similarity between different words from the input data directly and automatically by managing to collect the same topic sections. Further more, we propose a method to compute the passage similarity based on the word similarity. Finally we propose a method of topic segmentation based on the passage similarity in an unsupervised mode.