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[Keyword] discourse analysis(2hit)

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  • A Model of Discourse Segmentation and Segment Title Assignment for Lecture Speech Indexing

    Kazuhiro TAKEUCHI  Yukie NAKAO  Hitoshi ISAHARA  

     
    PAPER

      Vol:
    E90-D No:10
      Page(s):
    1601-1610

    Dividing a lecture speech into segments and providing those segments as learning objects are quite general and convenient way to construct e-learning resources. However it is difficult to assign an appropriate title to each object that reflects its content. Since there are various aspects of analyzing discourse segments, it is inevitable that researchers will face the diversity when describing the "meanings" of discourse segments. In this paper, we propose the assignment of discourse segment titles from the representation of their "meanings." In this assigning procedure, we focus on the speaker's evaluation for the event or the speech object. To verify the effectiveness of our idea, we examined identification of the segment boundaries from the titles that were described in our procedure. We confirmed that the result of the identification was more accurate than that of intuitive identification.

  • A Linguistic Procedure for an Extension Number Guidance System

    Naomi INOUE  Izuru NOGAITO  Masahiko TAKAHASHI  

     
    PAPER

      Vol:
    E76-D No:1
      Page(s):
    106-111

    This paper describes the linguistic procedure of our speech dialogue system. The procedure is composed of two processes, syntactic analysis using a finite state network, and discourse analysis using a plan recognition model. The finite state network is compiled from regular grammar. The regular grammar is described in order to accept sentences with various styles, for example ellipsis and inversion. The regular grammar is automatically generated from the skeleton of the grammar. The discourse analysis module understands the utterance, generates the next question for users and also predicts words which will be in the next utterance. For an extension number guidance task, we obtained correct recognition results for 93% of input sentences without word prediction and for 98% if prediction results include proper words.