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Improving the Performance of Korean Text Chunking by Machine learning Approaches based on Feature Set Selection  

Hwang, Young-Sook (Dept.of Computer Sceince, Korea University)
Chung, Hoo-jung (Dept.of Computer Sceince, Korea University)
Park, So-Young (Dept.of Computer Sceince, Korea University)
Kwak, Young-Jae (Dept.of Computer Sceince, Korea University)
Rim, Hae-Chang (Dept.of Computer Sceince, Korea University)
Publication Information
Journal of KIISE:Software and Applications / v.29, no.9, 2002 , pp. 654-668 More about this Journal
Abstract
In this paper, we present an empirical study for improving the Korean text chunking based on machine learning and feature set selection approaches. We focus on two issues: the problem of selecting feature set for Korean chunking, and the problem of alleviating the data sparseness. To select a proper feature set, we use a heuristic method of searching through the space of feature sets using the estimated performance from a machine learning algorithm as a measure of "incremental usefulness" of a particular feature set. Besides, for smoothing the data sparseness, we suggest a method of using a general part-of-speech tag set and selective lexical information under the consideration of Korean language characteristics. Experimental results showed that chunk tags and lexical information within a given context window are important features and spacing unit information is less important than others, which are independent on the machine teaming techniques. Furthermore, using the selective lexical information gives not only a smoothing effect but also the reduction of the feature space than using all of lexical information. Korean text chunking based on the memory-based learning and the decision tree learning with the selected feature space showed the performance of precision/recall of 90.99%/92.52%, and 93.39%/93.41% respectively.
Keywords
Korean Base Phrase Recognition; Machine Learning; Decision Tree; Feature Set Selection; Memory -based Learning;
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Times Cited By KSCI : 2  (Citation Analysis)
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