single-jc.php

JACIII Vol.10 No.6 pp. 859-867
doi: 10.20965/jaciii.2006.p0859
(2006)

Paper:

Dynamic Sense Representation Using Conceptual Fuzzy Sets

Hiroshi Sekiya, Takeshi Kondo, Makoto Hashimoto,
and Tomohiro Takagi

Department of Computer Science, Meiji University, 1-1-1 Higashi Mita, Tama-ku, Kawasaki-shi, Kanagawa 214-8571, Japan

Received:
January 10, 2006
Accepted:
April 14, 2006
Published:
November 20, 2006
Keywords:
conceptual fuzzy sets, word sense disambiguation
Abstract
The ambiguity in language is one of the most difficult problems in dealing with word senses using computers. Word senses vary dynamically depending on context. We need to specify the context to identify these. However, context also varies depending on granularity and the viewpoint of the topic. Therefore, generally speaking, people pay attention to the part of the attributes of the entity, which the dictionary definition of the word indicates, depending on such variant contexts. We call this “aspectual sense.” In this paper, we propose a method to represent such senses using conceptual fuzzy sets. First we generate atomic conceptual fuzzy sets automatically using word sequences just before the target word and the modified confabulation model (a prediction method similar to the n-gram model). Then we assign a word to the appropriate fuzzy set using a method based on co-occurrences. Based on an experiment using a large corpus, which was the AQUAINT collection consisting of 1 million newswire text data in English compiled from three sources, we generated each atomic conceptual fuzzy set expressed in the aspectual sense depending on variant contexts. Then we experimented using a few keywords, phrased like short queries, in a general information retrieval task, which is a difficult situation to extract context. The results of this task demonstrated that each assigned fuzzy set corresponding to context predicted by the few keywords was appropriate.
Cite this article as:
H. Sekiya, T. Kondo, M. Hashimoto, and T. Takagi, “Dynamic Sense Representation Using Conceptual Fuzzy Sets,” J. Adv. Comput. Intell. Intell. Inform., Vol.10 No.6, pp. 859-867, 2006.
Data files:
References
  1. [1] T. Takagi, A. Imura, H. Ushida, and T. Yamaguchi, “Conceptual Fuzzy Sets as a Meaning Representation and their Inductive Construction,” International Journal of Intelligent Systems, Vol.10, pp. 929-945, 1995.
  2. [2] T. Takagi, A. Imura, H. Ushida, and T. Yamaguchi, “Multi-layered Reasoning by Means of Conceptual Fuzzy Sets,” International Journal of Intelligent Systems, Vol.11, pp. 97-111, 1996.
  3. [3] G. Miller, “WordNet: An On-line Lexical Database,” International Journal of Lexicography, Vol.3(4), pp. 235-312, 1990.
  4. [4] E. M. Voorhees, “Using WordNet to Disambiguate Word Senses for Text Retrieval,” Proceedings of the 16th International ACM SIGIR Conference, Pittsburgh, PA, pp. 171-180, 1993.
  5. [5] C. Stokoe, M. P. Oakes, and J. Tait, “Word Sense Disambiguation in Information Retrieval Revisited,” Proceedings of the 26th Annual International ACM SIGIR Conference, Toronto, Canada, pp. 159-166, 2003.
  6. [6] F. Jelinek, “Self-organized language modeling for speech recognition,” Readings in Speech Recognition, Morgan Kaufmann Publishers, San Francisco, pp. 450-506, 1990.
  7. [7] The AQUAINT Corpus
    http://www.ldc.upenn.edu/Catalog/docs/LDC2002T31/
  8. [8] R. Krovetz, “Homonymy and Polysemy in Information Retrieval,” Proceedings of the 8th Conference on European Chapter of the Association for Computational Linguistics, pp. 72-79, 1997.
  9. [9] B. M. Slator and Y. A. Wilks, “Towards semantic structures from dictionary entries,” Proceedings of the 2nd Annual Rocky Mountain Conference on Artificial Intelligence, Boulder, Colorado, pp. 85-96, 1987.
  10. [10] A. Kilgarriff, “Dictionary Word Sense Distinctions: An enquiry into their nature,” Computers and the Humanities, 26, pp. 365-387, 1993.
  11. [11] D. Tufiş, R. Ion, and N. Ide, “Fine-Grained Word Sense Disambiguation Based on Parallel Corpora, Word Alignment, Word Clustering and Aligned Wordnets,” Proceedings of the 20th International Conference on Computational Linguistics, Geneva, pp. 1312-1318, 2004.
  12. [12] A. Meillet, “Linguistique historique et linguistique générale,”Vol.1, Champion, Paris, 1926.
  13. [13] L. Wittgenstein, “Philosophische Untersuchungen,” Suhrkamp, Frankfurt am Main, 1953.
  14. [14] A. E. Goldberg, “Constructions: A Construction Grammar Approach to Argument Structure,” University of Chicago Press, Chicago, 1995.
  15. [15] D. Yarowsky, “One Sense Per Collocation,” Proceedings of the ARPA Human Language Technology Workshop, pp. 266-271, 1993.
  16. [16] J. P. Callan, W. B. Croft, and J. Broglio, “TREC and TIPSTER Experiments with INQUERY,” Information Processing and Management, 31(3), pp. 327-343, 1994.
  17. [17] J. Xu and W. B. Croft, “Improving the effectiveness of information retrieval with local context analysis,” ACM Transactions on Information Systems (TOIS), Vol.18, Issue 1, pp. 79-112, 2000.
  18. [18] N. Ide and J. Véronis, “Word Sense Disambiguation: The State of The Art,” Computational Linguistics, Vol.24(1), pp. 1-41, 1998.
  19. [19] I. H. Witten and T. C. Bell, “The Zero-Frequency Problem: Estimating the Probabilities of Novel Events in AdaptiveText Compression,” IEEE Transactions on Information Theory, 37(4), pp. 1085-1094, 1991.
  20. [20] R. Hecht-Nielsen, “A Theory of Cerebral Cortex,” UCSD Institute for Neural Computation Technical Report #0401, 2004.
  21. [21] R. Hecht-Nielsen, “A Theory of Cerebral Cortex,” UCSD Institute for Neural Computation Technical Report #0404, 2004.
  22. [22] G. G. Turrigiano and S. B. Nelson, “Homeostatic Plasticity in the Developing Nervous System,” Nature Rev. Neurosci., 5, pp. 97-107, 2004.
  23. [23] G. G. Turrigiano and S. B. Nelson, “Hebb and Homeostasis in Neuronal Plasticity,” Curr. Opin. Neurobiol., 10, pp. 358-364, 2000.
  24. [24] G. G. Turrigiano, K. R. Leslie, N. S. Desai, L. C. Rutherford, and S. B. Nelson, “Activity-dependent Scaling of Quantal Amplitude in Neocortical Pyramidal Neurons,” Nature, 391, pp. 892-895, 1998.
  25. [25] N. S. Desai, R. H. Cudmore, S. B. Nelson, and G. G. Turrigiano, “Critical Periods for Experience-dependent Synaptic Scaling in Visual Cortex,” Nature Neurosci., 8, pp. 783-789, 2002.
  26. [26] E. Marder and A. A. Prinz, “Current Compensation in Neuronal Homeostasis,” Neuron, 37, pp. 2-4, 2003.
  27. [27] E. Marder and A. A. Prinz, “Modeling Stability in Neuron and Network Function: The Role of Activity in Homeostasis,” BioEssays, 24, pp. 1145-1154, 2002.
  28. [28] H. Sekiya, T. Kondo, M. Hashimoto, and T. Takagi, “Context Representation Using Word Sequences Extracted from a News Corpus,” Proc. of North American Fuzzy Information Processing Society Annual Conference (NAFIPS’05), 2005.
  29. [29] D. Yarowsky, “Decision Lists for Lexical Ambiguity Resolution: Application to Accent Restoration in Spanish and French,” Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, pp. 88-95, 1994.
  30. [30] D. Yarowsky, “A Comparison of Corpus-based Techniques for Restoring Accents in Spanish and French Text,” Proceedings of the 2nd Annual Workshop on Very Large Text Corpora, Las Cruces, pp. 19-32, 1994.
  31. [31] K. G. Dahlgren, “Naive Semantics for Natural Language Understanding,” Kluwer Academic Publishers, Boston, 1988.
  32. [32] H. Schütze and O. Pedersen, “Information Retrieval Based on Word Sense,” Proceedings of the Symposium on Document Analysis and Information Retrieval, pp. 161-175, 1995.
  33. [33] T. Kitagawa and Y. Kiyoki, “A mathematical model of meaning and its application to multidatabase systems,” Proceedings of 3rd IEEE International Workshop on Research Issues on Data Engineering: Interoperability in Multidatabase Systems, pp. 130-135, 1993.
  34. [34] A. Takano, Y. Niwa, S. Nishioka, M. Iwayama, T. Hisamitsu, O. Imaichi, and H. Sakurai, “Information Access Based on Associative Calculation,” SOFSEM 2000, LNCS Vol.1963, pp. 187-201, 2000.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Oct. 01, 2024