Making large-scale support vector machine learning practical
Advances in kernel methods
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A (acronyms)
ICE-TEA: in-context expansion and translation of English abbreviations
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
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A machine learning methodology for the disambiguation of acronym senses is presented, which starts from an acronym sense dictionary. Training data is automatically extracted from downloaded documents identified from the results of search engine queries. Leave-one-out cross-validation on 9,963 documents with 47 acronym forms achieves accuracy 92.58% and Fß=1=91.52%.