Abstract
This paper presented a novel word co-occurrence model, which was based on an ontology representation of word sense. In this study, word sense ontology is firstly constructed by context multi-elements, and then, the usage of word co-occurrence in content was gotten in using part of speech, semantic, location, average co-occurrence transition probabilities, and was expressed as word co-occurrence feature; final, word cohesion is calculated to judge the co-occurrence degree by the same co-occurrence feature. The relation experiments in natural language processing acquire better results.
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References
Kenneth, W.: Church and Patrick Hanks. Word association norms, mutual information and lexicography. In: Proceedings of the 27th Annual Conference of the Association of Computational Linguistics, pp. 76–82 (1989)
Matsuo, Y., Ishizuka, M.: Keyword extraction from a single document using word co-occurrence statistical information. International Journal on Artificial Intelligence Tools 13(1), 157–169 (2004)
Bullinaria, J.A., Levy, J.P.: Extracting Semantic Representations from Word Co-occurrence Statistics: A Computational Study, Behavior Research Methods (2006)
Mori, J., Matsuo, Y., Ishizuka, M.: Finding user semantics on the web using word co-occurrence information. In: PersWeb 2005. Proc. Int’l. Workshop on Personalization on the Semantic Web (2005)
Papka, R., Callan, J.P., Barto, A.G.: Text-Based Information Retrieval Using Exponentiated Gradient Descent. In: NIPS, pp. 3–9 (1996)
NIST: Automatic Evaluation of Machine Translation Quality Using N-gram Co-Occurrence Statistics (2002), http://www.nist.gov/speech/tests/mt/resources/scoring.htm
Lin, C.J., Lin, W.C., Bian, G.W., Chen, H.H.: Description of the NTU japanese-english cross-lingual information retrieval system. In: Proceedings of the NTCIR2 Workshop (1999)
Gao, J.F., Nie, J.Y., Zhang, J., Xun, E.D., Su, Y., Zhou, M., Huang, C.N.: TREC-9 CLIR experiments at MSRCN. In: The Nineth Text REtrieval Conference (November 2000)
Neches, R., Fikes, R., Finin, T., et al.: Enabling Technology for Knowledge Sharing. AI Magazine 12(3), 16–36 (1991)
Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing. In: International Workshop on Formal Ontology (1993)
CycL, http://www.cyc.com
W3C Semantic Web, http://www.w3.org/2001/sw
Uschold, M.: Building Ontologies-Towards A Unified Methodology. In: expert systems (1996)
Ontolingua, http://www.ksl.stanford.edu/software/
Dong, Zh.D.: http://www.keenage.com/
Lingpeng, Y., Donghong, J., Li, T.: Document Re-ranking Based on Automatically Acquired Key Terms in Chinese Information Retrieval[A]. In: Proceedings of the COLING, Sydney, pp. 480–486 (2004)
Salton, G., Buckley, C.: Term weighting approaches in automatic text retrieval. Information Processing and Management 24(5), 513–523 (1988)
Oleshchuk, V., Pedersen, A.: Ontology Based Semantic Similarity Comparison of Documents. In: 14th International Workshop on Database and Expert Systems Applications, pp. 735–738 (September 2003)
Besancon, R., Rajman, M., Chappelier, J.C.: Textual similarities based on a distributional approach. In: Tenth International Workshop on Database and Expert Systems Applications, pp. 180–184 (September 1-3, 1999)
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Zheng, D., Zhao, T., Li, S., Yu, H. (2007). Research on a Novel Word Co-occurrence Model and Its Application. In: Zhang, Z., Siekmann, J. (eds) Knowledge Science, Engineering and Management. KSEM 2007. Lecture Notes in Computer Science(), vol 4798. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76719-0_43
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DOI: https://doi.org/10.1007/978-3-540-76719-0_43
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