An edit distance approach to shallow semantic labeling
Refereed conference paper presented and published in conference proceedings


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AbstractThis paper proposes a model of semantic labeling based on the edit distance. The dynamic programming approach stresses on a non-exact string matching technique that takes full advantage of the underlying grammatical structure of 65,000 parse trees in a Treebank. Both part-of-speech and lexical similarity serve to identify the possible semantic labels, without miring into a pure linguistic analysis. The model described has been implemented. We also analyze the tradeoffs between the part-of-speech and lexical similarity in the semantic labeling. Experimental results for recognizing various labels in 10,000 sentences are used to justify its significances.
All Author(s) ListChan SWK
Name of Conference8th International Conference on Intelligent Data Engineering and Automated Learning
Start Date of Conference16/12/2007
End Date of Conference19/12/2007
Place of ConferenceBirmingham
Country/Region of ConferenceGreat Britain
Journal nameLecture Notes in Artificial Intelligence
Detailed descriptioned. by H. Yin, P. Tino & X. Yao.
Year2007
Month1
Day1
Volume Number4881
PublisherSPRINGER-VERLAG BERLIN
Pages57 - 66
ISBN978-3-540-77225-5
ISSN0302-9743
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Theory & Methods

Last updated on 2020-12-07 at 03:33