ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
SaRAD: a Simple and Robust Abbreviation Dictionary
Bioinformatics
A supervised learning approach to acronym identification
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
A Novel Method of Automobiles' Chinese Nickname Recognition
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
Challenges for extracting biomedical knowledge from full text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Supporting Systematic Reviews Using Text Mining
Social Science Computer Review
Seeking Acronym Definitions: a Web-based Approach
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Seeking Acronym Definitions: a Web-based Approach
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Automatically identifying the source words of lexical blends in english
Computational Linguistics
Automatic extraction of acronym definitions from the Web
Applied Intelligence
High-recall extraction of acronym-definition pairs with relevance feedback
Proceedings of the 2012 Joint EDBT/ICDT Workshops
An algorithm for local geoparsing of microtext
Geoinformatica
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We present a term recognition approach to extract acronyms and their definitions from a large text collection. Parenthetical expressions appearing in a text collection are identified as potential acronyms. Assuming terms appearing frequently in the proximity of an acronym to be the expanded forms (definitions) of the acronyms, we apply a term recognition method to enumerate such candidates and to measure the likelihood scores of the expanded forms. Based on the list of the expanded forms and their likelihood scores, the proposed algorithm determines the final acronym-definition pairs. The proposed method combined with a letter matching algorithm achieved 78% precision and 85% recall on an evaluation corpus with 4,212 acronym-definition pairs.