Initial experiments on automatic story segmentation in chinese spoken documents using lexical cohesion of extracted named entities

  • Authors:

  • Devon Li;Wai-Kit Lo;Helen Meng

  • Affiliations:

  • Human-Computer Communications Laboratory, Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong;Human-Computer Communications Laboratory, Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong;Human-Computer Communications Laboratory, Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong

  • Venue:
  • ISCSLP'06 Proceedings of the 5th international conference on Chinese Spoken Language Processing
  • Year:
  • 2006

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Abstract

Story segmentation plays a critical role in spoken document processing. Spoken documents often come in a continuous audio stream without explicit boundaries related to stories or topics. It is important to be able to automatically segment these audio streams into coherent units. This work is an initial attempt to make use of informative lexical terms (or key terms) in recognition transcripts of Chinese spoken documents for story segmentation. This is because changes in the distribution of informative terms are generally associated with story changes and topic shifts. Our methods of information lexical term extraction include the extraction of POS-tagged nouns, as well as a named entity identifier that extracts Chinese person names, transliterated person names, location and organization names. We also adopted a lexical chaining approach that links up sentences that are lexically “coherent” with each other. This leads to the definition of a lexical chain score that is used for story boundary hypothesis. We conducted experiments on the recognition transcripts of the TDT2 Voice of America Mandarin speech corpus. We compared among several methods of story segmentation, including the use of pauses for story segmentation, the use of lexical chains of all lexical entries in the recognition transcripts, the use of lexical chains of nouns tagged by a part-of-speech tagger, as well as the use of lexical chains of extracted named entities. Lexical chains of informative terms, namely POS-tagged nouns and named entities were found to give comparable performance (F-measures of 0.71 and 0.73 respectively), which is superior to the use of all lexical entries (F-measure of 0.69).