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Interpolated probabilistic tagging model optimized with genetic algorithm
Wong F.1; Chao S.1; Hu D.-C.2; Mao Y.-H.2
2004-11-02
Conference NameInternational Conference on Machine Learning and Cybernetics
Source PublicationProceedings of 2004 International Conference on Machine Learning and Cybernetics
Volume4
Pages2569-2574
Conference DateAUG 26-29, 2004
Conference PlaceShanghai, PEOPLES R CHINA
Abstract

In this paper, we present results of probabilistic tagging of Portuguese texts in order to show how these techniques work for one of the highly morphologically ambiguous inflective languages by using a limited corpus as the basic training source. In order to cope the ambiguities problem caused by the insufficient training data, especially the unknown words, we incorporate the lexical features into the probabilistic model. Different from other proposed tagging models, these features are introduced into the word probabilities by means of interpolation. A technique to determine the optimal set of interpolation parameters based on genetic algorithm is described. Our preliminary result shows that we can correctly tag 91.8% of the sentences based on our tagging model.

KeywordGenetic Algorithm Portuguese Tagging Pos Tagging Probabilistic Model
DOI10.1109/ICMLC.2004.1382237
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics ; Computer Science, Information Systems
WOS IDWOS:000225293600508
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Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Universidade de Macau
2.Tsinghua University
Recommended Citation
GB/T 7714
Wong F.,Chao S.,Hu D.-C.,et al. Interpolated probabilistic tagging model optimized with genetic algorithm[C],2004:2569-2574.
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