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Geyken/etal/2015a: Using a Maximum Entropy Classifier to link “good” corpus examples to dictionary senses

Bibtype Incollection
Bibkey Geyken/etal/2015a
Author Alexander Geyken, Christian Pölitz und Thomas Bartz
Ls8autor Bartz, Thomas
Pölitz, Christian
Editor Kosem, Iztok; Jakubíček, Miloš; Kallas, Jelena and Krek, Simon
Title Using a Maximum Entropy Classifier to link “good” corpus examples to dictionary senses
Booktitle Electronic lexicography in the 21st century: linking lexical data in the digital age. Proceedings of the eLex 2015 conference, 11-13 August 2015, Herstmonceux Castle, United Kingdom
Pages 304-314
Address Ljubljana and Brighton
Publisher Trojina, Institute for Applied Slovene Studies and Lexical Computing Ltd.
Abstract A particular problem of maintaining dictionaries consists of replacing outdated example sentences by corpus examples that are up-to-date. Extraction methods such as the good example finder (GDEX; Kilgarriff, 2008) have been developed to tackle this problem. We extend GDEX to polysemous entries by applying machine learning techniques in order to map the example sentences to the appropriate dictionary senses. The idea is to enrich our knowledge base by computing the set of all collocations and to use a maximum entropy classifier (MEC; Nigam, 1999) to learn the correct mapping between corpus sentence and its correct dictionary sense. Our method is based on hand labeled sense annotations. Results reveal an accuracy of 49.16% (MEC) which is significantly better than the Lesk algorithm (31.17%).
Year 2015
Projekt Kobra
Url https://elex.link/elex2015/proceedings/eLex_2015_19_Geyken+Politz+Bartz.pdf
 


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