Abstract：Based on the problems in typical term context similarity algorithm, the paper puts forward a new term similarity algorithm which constructs context patterns automatically by sentences dependencies analysis and then computes term similarity by mapping context patterns. The algorithm provides a better way to construct term context patterns. Meanwhile, term context characters are kept well in patterns. The paper also presents the specific implementation steps of new algorithm, and evaluates the algorithm on basis of gene engineering field experiment data set. Experiment result demonstrates that the algorithm has an obvious improvement in computing performance.
徐健. 基于句法依赖关系模板的术语相似度计算方法[J]. 现代图书情报技术, 2011, 27(9): 28-33.
Xu Jian. A Term Similarity Algorithm Based on Context Dependency Relation Pattern. New Technology of Library and Information Service, 2011, 27(9): 28-33.
[1] Chen P, Lin S. Automatic Keyword Prediction Using Google Similarity Distance[J]. Expert Systems with Applications, 2010, 37(3): 1928-1938.[2] Shehata S. A WordNet-based Semantic Model for Enhancing Text Clustering[C]. In:Proceedings of the 2009 IEEE International Conference on Data Mining Workshops. 2009: 477-482.[3] Aimé X, Furst F, Kuntz P, et al. SemioSem: A Semiotic-based Similarity Measure[C]. In: Proceedings of the Confederated International Workshops and Posters on the Move to Meaningful Internet Systems: ADI, CAMS, EI2N, ISDE, IWSSA, MONET, OnToContent, ODIS, ORM, OTM Academy, SWWS, SEMELS, Beyond SAWSDL, and COMBEK 2009. 2009: 584-593.[4] [JP3]Dong H, Hussain F K, Chang E. A Hybrid Concept Similarity Measure Model for Ontology Environment[C]. In: Proceedings of the Confederated International Workshops and Posters on the Move to Meaningful Internet Systems: ADI, CAMS, EI2N, ISDE, IWSSA, MONET, OnToContent, ODIS, ORM, OTM Academy, SWWS, SEMELS, Beyond SAWSDL, and COMBEK 2009. 2009: 848-857.[JP][5] Neshati M, Hassanabadi L S. Taxonomy Construction Using Compound Similarity Measure[C]. In: Proceedings of the 2007 OTM Confederated International Conference on the Move to Meaningful Internet Systems: CoopIS, DOA, ODBASE, GADA, and IS. 2007:915-932.[6] Hindle D. Noun Classification from Predicate-argument Structures[C]. In: Proceedings of the 28th Annual Meeting on Association for Computational Linguistics.1990:268-275.[7] Church K W, Hanks P. Word Association Norms, Mutual Information, and Lexicography[C]. In: Proceedings of the 27th Annual Meeting of ACL, Vancouver. 1989: 76-83.[8] [JP3]Nenadic G, Spasic I, Ananiadou S. Automatic Discovery of Term Similarities Using Pattern Mining[C]. In: Proceedings of the 2nd International Workshop on Computational Terminology. 2002: 43-49. [JP][9] Stanford Dependencies [EB/OL]. [2011-07-15]. http://nlp.stanford.edu/software/stanford-dependencies.shtml.[10] 徐健. 过滤阶段需保留的依赖关系类型列表[EB/OL]. [2011-07-15]. http://blog.sina.com.cn/s/blog_64b661270100 u8iz.html.[11] De Marneffe M C, Manning C D. Stanford Typed Dependencies Manual[EB/OL]. [2011-07-15]. http://nlp.stanford.edu/software/dependencies_manual.pdf.[12] Bollegala D, Matsuo Y, Ishizuka M. Measuring Semantic Similarity Between Words Ysing Web Search Engines[C]. In: Proceedings of International World Wide Web Conference Committee(WWW 2007), Banff, Alberta, Canada.2007:757-766.[13] [JP3]Turney P D. Mining the Web for Synonyms: PMI-IR Versus LSA on TOEFL[C]. In: Proceedings of the 12th European Conference on Machine Learning. London, UK: Springer-Verlag, 2001:491-502.[JP][14] Precision and Recall[EB/OL]. [2010-09-08]. http://en.wikipedia.org/wiki/Precision_and_recall.
