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Adaptive transformation-based learning for improving dictionary tagging

  • University of Maryland, College Park

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We present an adaptive technique that enables users to produce a high quality dictionary parsed into its lexicographic components (headwords, pronunciations, parts of speech, translations, etc.) using an extremely small amount of user provided training data. We use transformation-based learning (TBL) as a postprocessor at two points in our system to improve performance. The results using two dictionaries show that the tagging accuracy increases from 83% and 91% to 93% and 94% for individual words or "tokens", and from 64% and 83% to 90% and 93% for contiguous "phrases" such as definitions or examples of usage.

Original languageEnglish
Title of host publicationEACL 2006 - 11th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
Pages257-264
Number of pages8
StatePublished - 2006
Event11th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2006 - Trento, Italy
Duration: Apr 3 2006Apr 7 2006

Publication series

NameEACL 2006 - 11th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference

Conference

Conference11th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2006
Country/TerritoryItaly
CityTrento
Period04/3/0604/7/06

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