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Remembrance of discourse based on textual continuity: A spreading activation network

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Book cover PRICAI'96: Topics in Artificial Intelligence (PRICAI 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1114))

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Abstract

In this paper, we present a computational model for transforming discourses into Quasi-Mental Clusters (QMCs) through a convergence process. The process is interpreted as a particular transformation of a given set of discourse segments and concepts by examining the textual continuity. Examinations include testing the local cohesion in a cohesion parsing as well as the global coherence in semantic decomposition. In the convergence process, sentences in a discourse are represented as nodes in a spreading activation network. Competing coalitions of the nodes drive the network into a stable equilibrium. We argue the resulting QMCs are useful data structures in remembrance, summarization and knowledge discovery in discourses.

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Authors and Affiliations

  1. School of Computer Science & Engineering, The University of New South Wales, 2052, Sydney, NSW, Australia

    Samuel W. K. Chan & James Franklin

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  1. Samuel W. K. Chan
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  2. James Franklin
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Norman Foo Randy Goebel

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© 1996 Springer-Verlag Berlin Heidelberg

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Chan, S.W.K., Franklin, J. (1996). Remembrance of discourse based on textual continuity: A spreading activation network. In: Foo, N., Goebel, R. (eds) PRICAI'96: Topics in Artificial Intelligence. PRICAI 1996. Lecture Notes in Computer Science, vol 1114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61532-6_19

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  • DOI: https://doi.org/10.1007/3-540-61532-6_19

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61532-3

  • Online ISBN: 978-3-540-68729-0

  • eBook Packages: Springer Book Archive

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