Terminological variation, a means of identifying research topics from texts
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Information extraction with automatic knowledge expansion
Information Processing and Management: an International Journal
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Complex structuring of term variants for Question Answering
MWE '03 Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment - Volume 18
A Taxonomy Learning Method and Its Application to Characterize a Scientific Web Community
IEEE Transactions on Knowledge and Data Engineering
Semantic business process integration based on ontology alignment
Expert Systems with Applications: An International Journal
Word Sense Disambiguation Based on Wikipedia Link Structure
ICSC '09 Proceedings of the 2009 IEEE International Conference on Semantic Computing
Reusing ontology mappings for query routing in semantic peer-to-peer environment
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A term normalization method for better performance of terminology construction
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
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The importance of research on knowledge management is growing due to recent issues on Big Data. One of the most fundamental steps in knowledge management is the extraction of terminologies. Terms are often expressed in various forms and the variations often play a negative role, becoming an obstacle which causes knowledge systems to extract unnecessary ones. To solve the problem, we propose a method of term normalization which finds a normalized form (original and standard form defined in dictionaries) of variant terms. The method employs two characteristics of terms: appearance similarity measuring how similar terms are, context similarity measuring how many clue words they share. Through experiment, we show its positive influence of both similarities in term normalization.