Abstract
In order to construct story databases, it is crucial to have an effective index that represents the plot and event sequences in a document. For this purpose, we have already proposed a method using the concept of maximal analogy to represent a generalized event sequence of documents with a maximal set of events. However, it is expensive to calculate a maximal analogy from documents with a large number of sentences. Therefore, in this paper, we propose an efficient algorithm to generate a maximal analogy, based on graph theory, and we confirm its effectiveness experimentally. We also discuss how to use a maximal analogy as an index for a story database, and outline our future plans.
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References
Mani, I.: Automatic Summarization. John Benjamin Publishing Company (2001)
Firmin, T., Chrzanowski, M.J.: An evaluation of automatic text summarization systems. In: Mani, I., Maybury, M.T. (eds.) Advances in automatic text summarization, pp. 325–340. The MIT Press, Cambridge (1999)
Haraguchi, M., Nakano, S., Yoshioka, M.: Discovery of maximal analogies between stories. In: Lange, S., Satoh, K., Smith, C.H. (eds.) DS 2002. LNCS, vol. 2534, pp. 324–331. Springer, Heidelberg (2002)
Ohsawa, Y., Benson, N.E., Yachida, M.: Keygraph: Automatic indexing by cooccurrence graph based on building construction metaphor. In: Proceedings of the Advances in Digital Libraries Conference, pp. 12–18 (1998)
Japan Electronic Dictionary Research Institute, Ltd. (EDR): EDR Electronic Dictionary Version 2.0 Technical Guide TR2-007 (1998)
Cohen, W.W., Hirsh, H.: The learnability of description logics with equality constraints. Machine Learning 17, 169–199 (1994)
Sowa, J.F. (ed.): Principles of Semantic Networks. Morgan Kaufmann, San Francisco (1991)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proc. 20th Int. Conf. Very Large Data Bases, VLDB, pp. 487–499. Morgan Kaufmann, San Francisco (1994)
Kudo, T., Matsumoto, Y.: Japanese dependency analysis using cascaded chunking. In: CoNLL 2002: Proceedings of the 6th Conference on Natural Language Learning 2002 (COLING 2002 Post-Conference Workshops), pp. 63–69 (2002)
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Yoshioka, M., Haraguchi, M., Mizoe, A. (2005). Towards Constructing Story Databases Using Maximal Analogies Between Stories. In: Grieser, G., Tanaka, Y. (eds) Intuitive Human Interfaces for Organizing and Accessing Intellectual Assets. Lecture Notes in Computer Science(), vol 3359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32279-5_17
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DOI: https://doi.org/10.1007/978-3-540-32279-5_17
Publisher Name: Springer, Berlin, Heidelberg
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