ABSTRACT
We present a domain-independent topic segmentation algorithm for multi-party speech. Our feature-based algorithm combines knowledge about content using a text-based algorithm as a feature and about form using linguistic and acoustic cues about topic shifts extracted from speech. This segmentation algorithm uses automatically induced decision rules to combine the different features. The embedded text-based algorithm builds on lexical cohesion and has performance comparable to state-of-the-art algorithms based on lexical information. A significant error reduction is obtained by combining the two knowledge sources.
- D. Beeferman, A. Berger, and J. Lafferty. 1999. Statistical models for text segmentation. Machine Learning, 34(1--3):177--210. Google ScholarDigital Library
- F. Choi. 2000. Advances in domain independent linear text segmentation. In Proc. of NAACL'00. Google ScholarDigital Library
- W. Cochran. 1950. The comparison of percentages in matched samples. Biometrika, 37:256--266.Google ScholarCross Ref
- B. Grosz and J. Hirschberg. 1992. Some intonational characteristics of discourse structure. In Proc. of ICSLP-92, pages 429--432.Google Scholar
- B. Grosz and C. Sidner. 1986. Attention, intentions and the structure of discourse. Computational Linguistics, 12(3). Google ScholarDigital Library
- M. Hajime, H. Takeo, and O. Manabu. 1998. Text segmentation with multiple surface linguistic cues. In COLING-ACL, pages 881--885. Google ScholarDigital Library
- M. Hearst. 1994. Multi-paragraph segmentation of expository text. In Proc. of the ACL. Google ScholarDigital Library
- J. Hirschberg and D. Litman. 1994. Empirical studies on the disambiguation of cue phrases. Computational Linguistics, 19(3):501--530. Google ScholarDigital Library
- J. Hirschberg and C. Nakatani. 1996. A prosodic analysis of discourse segments in direction-giving monologues. In Proc. of the ACL. Google ScholarDigital Library
- J.Hirschberg and C. Nakatani. 1998. Acoustic indicators of topic segmentation. In Proc. of ICSLP.Google Scholar
- A. Janin, D. Baron, J. Edwards, D. Ellis, D. Gelbart, N. Morgan, B. Peskin, T. Pfau, E. Shriberg, A. Stolcke, and C. Wooters. 2003. The ICSI meeting corpus. In Proc. of ICASSP-03, Hong Kong (to appear).Google Scholar
- M.-Y. Kan, J. Klavans, and K. McKeown. 1998. Linear segmentation and segment significance. In Proc. 6th Workshop on Very Large Corpora (WVLC-98).Google Scholar
- H. Kozima. 1993. Text segmentation based on similarity between words. In Proc. of the ACL. Google ScholarDigital Library
- S. Levinson. 1983. Pragmatics. Cambridge University Press.Google Scholar
- D. Litman and R. Passonneau. 1995. Combining multiple knowledge sources for discourse segmentation. In Proc. of the ACL. Google ScholarDigital Library
- J. Morris and G. Hirst. 1991. Lexcial cohesion computed by thesaural relations as an indicator of the structure of text. Computational Linguistics, 17:21--48. Google ScholarDigital Library
- C. Nakatani, J. Hirschberg, and B. Grosz. 1995. Discourse structure in spoken language: Studies on speech corpora. In AAAI-95 Symposium on Empirical Methods in Discourse Interpretation.Google Scholar
- R. Passonneau and D. Litman. 1993. Intention-based segmentation: Human reliability and correlation with linguistic cues. In Proc. of the ACL. Google ScholarDigital Library
- R. Passonneau and D. Litman. 1997. Discourse segmentation by human and automated means. Computational Linguistics, 23(1):103--139. Google ScholarDigital Library
- L. Pevzner and M. Hearst. 2002. A critique and improvement of an evaluation metric for text segmentation. Computational Linguistics, 28 (1):19--36. Google ScholarDigital Library
- R. Quinlan. 1993. C4.5: Programs for Machine Learning. Machine Learning. Morgan Kaufmann. Google ScholarDigital Library
- J. Reynar. 1994. An automatic method of finding topic boundaries. In Proc. of the ACL. Google ScholarDigital Library
- J. Reynar. 1999. Statistical models for topic segmentation. In Proc. of the ACL. Google ScholarDigital Library
- G. Salton and C. Buckley. 1988. Term weighting approaches in automatic text retrieval. Information Processing and Management, 24(5):513--523. Google ScholarDigital Library
- G. Tür, D. Hakkani-Tür, A. Stolcke, and E. Shriberg. 2001. Integrating prosodic and lexical cues for automatic topic segmentation. Computational Linguistics, 27(1):31--57. Google ScholarDigital Library
- M. Utiyama and H. Isahara. 2001. A statistical model for domain-independent text segmentation. In Proc. of the ACL. Google ScholarDigital Library
- J. Xu and B. Croft. 1998. Corpus-based stemming using cooccurrence of word variants. ACM Transactions on Information Systems, 16(1):61--81. Google ScholarDigital Library
- Discourse segmentation of multi-party conversation
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