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Terminology mining, term extraction, term recognition, or glossary extraction, is a subtask of information extraction. The goal of terminology extraction is to automatically extract relevant terms from a given corpus. In the semantic web era, a growing number of communities and networked enterprises started to access and interoperate through the internet. Modeling these communities and their information needs is important for several web applications, like topic-driven web crawlers,〔Menczer F., Pant G. and Srinivasan P. (Topic-Driven Crawlers: machine learning issues ).〕 web services,〔Fan J. and Kambhampati S. (A Snapshot of Public Web Services ), in ACM SIGMOD Record archive Volume 34 , Issue 1 (March 2005).〕 recommender systems,〔Yan Zheng Wei, Luc Moreau, Nicholas R. Jennings. (A market-based approach to recommender systems ), in ACM Transactions on Information Systems (TOIS), 23(3), 2005.〕 etc. The development of terminology extraction is essential to the language industry. One of the first steps to model the knowledge domain of a virtual community is to collect a vocabulary of domain-relevant terms, constituting the linguistic surface manifestation of domain concepts. Several methods to automatically extract technical terms from domain-specific document warehouses have been described in the literature.〔Bourigault D. and Jacquemin C. (Term Extraction+Term Clustering: an integrated platform for computer-aided terminology ), in Proc. of EACL, 1999.〕〔K. Frantzi, S. Ananiadou and H. Mima. (2000). (Automatic recognition of multi-word terms: the C-value/NC-value method. ) In: C. Nikolau and C. Stephanidis (Eds.) International Journal on Digital Libraries, Vol. 3, No. 2., pp. 115-130.〕〔K. Frantzi, S. Ananiadou and J. Tsujii. (1998) (The C-value/NC-value Method of Automatic Recognition of Multi-word Terms ), In: ECDL '98 Proceedings of the Second European Conference on Research and Advanced Technology for Digital Libraries, pp. 585-604. (3-540-65101-2 )〕〔Navigli R. and Velardi, P. (Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites ). Computational Linguistics. 30 (2), MIT Press, 2004, pp. 151-179〕〔Y. Park, R. J. Byrd, B. Boguraev. ("Automatic glossary extraction: beyond terminology identification" ), International Conference On Computational Linguistics, Proceedings of the 19th international conference on Computational linguistics - Taipei, Taiwan, 2002.〕〔(Sclano, F. ) and (Velardi, P. ). (TermExtractor ): a Web Application to Learn the Shared Terminology of Emergent Web Communities. To appear in Proc. of the 3rd International Conference on Interoperability for Enterprise Software and Applications (I-ESA 2007). Funchal (Madeira Island), Portugal, March 28–30th, 2007.〕〔P. Velardi, R. Navigli, P. D'Amadio. (Mining the Web to Create Specialized Glossaries ), IEEE Intelligent Systems, 23(5), IEEE Press, 2008, pp. 18-25.〕〔Wermter J. and Hahn U. (Finding New terminology in Very large Corpora ), in Proc. of K-CAP'05, October 2–5, 2005, Banff, Alberta, Canada〕〔Wong, W., Liu, W. & Bennamoun, M. (2007) (Determining Termhood for Learning Domain Ontologies using Domain Prevalence and Tendency ). In: 6th Australasian Conference on Data Mining (AusDM); Gold Coast. (978-1-920682-51-4 )〕〔Wong, W., Liu, W. & Bennamoun, M. (2007) (Determining Termhood for Learning Domain Ontologies in a Probabilistic Framework ). In: 6th Australasian Conference on Data Mining (AusDM); Gold Coast. (978-1-920682-51-4 )〕 Typically, approaches to automatic term extraction make use of linguistic processors (part of speech tagging, phrase chunking) to extract terminological candidates, i.e. syntactically plausible terminological noun phrases, NPs (e.g. compounds "credit card", adjective-NPs "local tourist information office", and prepositional-NPs "board of directors" - in English, the first two constructs are the most frequent). Terminological entries are then filtered from the candidate list using statistical and machine learning methods. Once filtered, because of their low ambiguity and high specificity, these terms are particularly useful for conceptualizing a knowledge domain or for supporting the creation of a domain ontology or a terminology base. Furthermore, terminology extraction is a very useful starting point for semantic similarity, knowledge management, human translation and machine translation, etc. ==Bilingual terminology extraction== The methods for terminology extraction can be applied to parallel corpora. Combined with e.g. co-occurrence statistics, candidates for term translations can be obtained.〔Macken, Lieve, Els Lefever, and Veronique Hoste. "TExSIS: Bilingual terminology extraction from parallel corpora using chunk-based alignment." Terminology 19.1 (2013): 1-30.〕 Bilingual terminology can be extracted also from comparable corpora〔Rapp, Reinhard, Pierre Zweigenbaum, and Pascale Fung. Building and Using Comparable Corpora. Springer, 2013.〕 (corpora containing texts within the same text type, domain but not translations of documents between each other). 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Terminology extraction」の詳細全文を読む スポンサード リンク
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