World Library  
Flag as Inappropriate
Email this Article

Terminology extraction

Article Id: WHEBN0008722168
Reproduction Date:

Title: Terminology extraction  
Author: World Heritage Encyclopedia
Language: English
Subject: Natural language processing, Terminology, Ontology learning, Pootle, NooJ
Publisher: World Heritage Encyclopedia

Terminology extraction

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,[1] web services,[2] recommender systems,[3] 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.[4][5][6][7][8][9][10][11][12][13][14][15][16]

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. Furthermore, terminology extraction is a very useful starting point for semantic similarity, knowledge management, human translation and machine translation, etc.

See also


  1. ^ Menczer F., Pant G. and Srinivasan P. Topic-Driven Crawlers: machine learning issues.
  2. ^ Fan J. and Kambhampati S. A Snapshot of Public Web Services, in ACM SIGMOD Record archive Volume 34 , Issue 1 (March 2005).
  3. ^ 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.
  4. ^ Bourigault D. and Jacquemin C. Term Extraction+Term Clustering: an integrated platform for computer-aided terminology, in Proc. of EACL, 1999.
  5. ^ Collier, N.; Nobata, C.; Tsujii, J. (2002). "Automatic acquisition and classification of terminology using a tagged corpus in the molecular biology domain". Terminology 7 (2): 239–257.  
  6. ^ 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.
  7. ^ 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. [isbn: 3-540-65101-2]
  8. ^ L. Kozakov, Y. Park, T. Fin, Y. Drissi, Y. Doganata, and T. Cofino. (2004). "Glossary extraction and utilization in the information search and delivery system for IBM Technical Support". IBM System Journal 43 (3). 
  9. ^ 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
  10. ^ 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.
  11. ^ 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.
  12. ^ 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.
  13. ^ 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
  14. ^ 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. [isbn: 978-1-920682-51-4]
  15. ^ 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. [isbn: 978-1-920682-51-4]
  16. ^ Wong, W., Liu, W. & Bennamoun, M. (2008). "Determination of Unithood and Termhood for Term Recognition". In M. Song and Y. Wu. Handbook of Research on Text and Web Mining Technologies. IGI Global.  

External links

This article was sourced from Creative Commons Attribution-ShareAlike License; additional terms may apply. World Heritage Encyclopedia content is assembled from numerous content providers, Open Access Publishing, and in compliance with The Fair Access to Science and Technology Research Act (FASTR), Wikimedia Foundation, Inc., Public Library of Science, The Encyclopedia of Life, Open Book Publishers (OBP), PubMed, U.S. National Library of Medicine, National Center for Biotechnology Information, U.S. National Library of Medicine, National Institutes of Health (NIH), U.S. Department of Health & Human Services, and, which sources content from all federal, state, local, tribal, and territorial government publication portals (.gov, .mil, .edu). Funding for and content contributors is made possible from the U.S. Congress, E-Government Act of 2002.
Crowd sourced content that is contributed to World Heritage Encyclopedia is peer reviewed and edited by our editorial staff to ensure quality scholarly research articles.
By using this site, you agree to the Terms of Use and Privacy Policy. World Heritage Encyclopedia™ is a registered trademark of the World Public Library Association, a non-profit organization.

Copyright © World Library Foundation. All rights reserved. eBooks from Project Gutenberg are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.