Abstract
By the development of the computer in recent years, calculating a complex advanced processing at high speed has become possible. Moreover, a lot of linguistic knowledge is used in the natural language processing system for improving the system. Therefore, the necessity of co-occurrence word information in the natural language processing system increases further and various researches using co-occurrence word information are done. Moreover, in the natural language processing, dictionary is necessary and indispensable because the ability of the entire system is controlled by the amount and the quality of the dictionary. In this paper, the importance of co-occurrence word information in the natural language processing system was described. The classification technique of the co-occurrence word (receiving word) and the co-occurrence frequency was described and the classified group was expressed hierarchically. Moreover, this paper proposes a technique for an automatic construction system and a complete thesaurus. Experimental test operation of this system and effectiveness of the proposal technique is verified.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Atlam, E.-S., Fuketa, M., Morita, K., Aoe, J.: Similarity measurement using term negative weight to word Similarity. Information Processing & Management Journal 36, 717–736 (2000)
Atlam, E.-S., Fuketa, M., Morita, K., Aoe, J.: Document Similarity measurement using field Association term. Information Processing & Management Journal 39, 809–824 (2003)
Fuketa, M., Lee, S., Tsuji, T., Okada, M., Aoe, J.: A document Classification method by using field association words. Information Science Journal 126, 57–70 (2000)
Fukumoto, F., Tsuji, J.: Cancellation of polysemy of verb based on corpus. Electronic information communication society technology research report NLC94-24, 15–22 (1994)
Hindle, D.: Noun Classification from redicate-argument structures. In: Proceedings of the 29th Annual meeting of the Association for Computational Linguistics, pp. 229–236 (1990)
Hirao, K., Matsumoto, Y.: Case frame acquisition of verb from corpus and clustering of noun. Information Processing Society of Japan research NL104-11, 79–86 (1994)
Koyama, M., Aoe, J.: High speed searching algorithm of hierarchy concept dictionary. In: The 51st national athletic meeting, vol. 7 E-2, pp. 4-235-4-236 (1995)
Kobayashi, Y., Tokunaga, K., Tanaka, H.: Analysis of compound noun that uses meaning co-occurrence information between nouns. Natural language processing 3(1), 29–43 (1996)
Li, H., Abe, N.: Clustering Words with the MDL Principle. Journal of Natural Language Processing 4(2), 71–87 (1997)
Morimoto, K., Iriguchi, H., Aoe, J.: A Retrieval Algorithm of Dictionaries by Using Two Trie Structures” (in Japanese). Trans. IEICE J76-D-II (11), 2374–2383
Morita, K., Mochizuki, H., Yoshihiro, Y., Aoe, J.: Efficient retrieval algorithm of co-occurrence information that uses trie structure. Information Processing Society of Japan thesis magazine 39(9), 2563–2571 (1998)
Yokoyama, H., Shinichiro, O.: Classification of meaning of Japanese verb that uses co-occurrence information, Electronic information communication society, pp. 1–8 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Atlam, ES., Ghada, E., Fuketa, M., Morita, K., Aoe, Ji. (2004). New Hierarchy Technique Using Co-Occurrence Word Information. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_75
Download citation
DOI: https://doi.org/10.1007/978-3-540-30132-5_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23318-3
Online ISBN: 978-3-540-30132-5
eBook Packages: Springer Book Archive