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Intra-Sentence Segmentation using Maximum Entropy Model for Efficient Parsing of English Sentences  

Kim Sung-Dong (한성대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.32, no.5, 2005 , pp. 385-395 More about this Journal
Abstract
Long sentence analysis has been a critical problem in machine translation because of high complexity. The methods of intra-sentence segmentation have been proposed to reduce parsing complexity. This paper presents the intra-sentence segmentation method based on maximum entropy probability model to increase the coverage and accuracy of the segmentation. We construct the rules for choosing candidate segmentation positions by a teaming method using the lexical context of the words tagged as segmentation position. We also generate the model that gives probability value to each candidate segmentation positions. The lexical contexts are extracted from the corpus tagged with segmentation positions and are incorporated into the probability model. We construct training data using the sentences from Wall Street Journal and experiment the intra-sentence segmentation on the sentences from four different domains. The experiments show about $88\%$ accuracy and about $98\%$ coverage of the segmentation. Also, the proposed method results in parsing efficiency improvement by 4.8 times in speed and 3.6 times in space.
Keywords
intra-sentence segmentation; maximum entropy model; parsing; machine translation;
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Times Cited By KSCI : 1  (Citation Analysis)
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