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Mining Acronym Expansion from Web

R. Menaha, J. Meenaakshi, P. Mohanapriya, V. Nandhini

Abstract


Acronyms are extensively used in web search. Typically acronym is an ambiguous one, when a user gives acronym as search query the search engine returns the results even it is not related to the user intent. To address this problem a method is proposed in this paper that, how to get the user desired web pages of the given acronym search query. The method uses two approaches they are i. Discover all multifarious definitions for the given acronym query ii.Finding popularity score and context words of each acronym definition. Acronym definitions are extracted from Google snippets, titles and acronym finder. Popularity score and context words are identified from query log files. The results evince that the proposed method performance is better than the existing system.


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References


S. Chaudhuri, V. Ganti, and D. Xin.Mining document collections to facilitate accurate approximate entity matching.PVLDB, 2(1), 2009.

T. Cheng, H. W. Lauw, and S. Paparizos. Entity synonyms for structured web search. TKDE, 2011.

D. Defays. An efficient algorithm for a complete link method. The Computer Journal, 20(4):364–366, 1977.

A. Jain, S. Cucerzan, and S. Azzam.Acronym-expansion recognition and ranking on the web. In Information Reuse and Integration, 2007

L. S. Larkey, P. Ogilvie, M. A. Price, and B. Tamilio.Acrophile: an automated acronym extractor and server. In Proceedings of the fifth ACM conference on Digital libraries, pages 205–214, 2000.

Min song, II-Yeol song, Ki Jung Lee (2006)’Automatic Extraction for Creating a Lexical Repository of Definitions in the Biomedical Literature’,volume 4081.

D. Nadeau and P. D. Turney. A supervised learning approach to acronym identification. In Proceedings of the 18th Canadian Society conference on Advances inArtificial Intelligence, pages 319–329, 2005.

M.Priya, S.SuganyaR.ElakkiyaandR.Menaha* An Efficient Approach for Improving Query Expansion of Acronyms using Abbreviations

Rio de Janeiro, Brazil.Mining Acronym Expansions and Their Meanings Using Query Click Log WWW 2013, May 13–17, 2013 ACM

P. D. Turney. Mining the web for synonyms: Pmi-irversuslsa on toefl.CoRR.cs.LG/0212033, 2002.

J. Wren, H. Garner, et al. Heuristics for identification of acronym-definition patterns within text: towards an automated construction of comprehensive acronym-definition dictionaries. Methods of information in medicine, 41(5):426–434, 2002.

Yeates S (1999) ‘Automatic extraction of acronyms from text.’In: Yeates S (ed.) Procofthird New Zealand computer science research students’ conference.University of Waikato, TeKohingaMaramaMarae, Hamilton, New Zealand, pp 117–124.

M. Zahariev. Efficient acronym-expansion matching for automatic acronym acquisition. In International Conference on Information and Knowledge Engineering, 2003

R. Jones, B. Rey, O. Madani, and W. Greiner. Generating query substitutions.In WWW, 2006.

D. Nadeau and P. D. Turney. A supervised learning approach to acronym identification. In Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence, pages 319–329, 2005.


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