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A Supervised Way in Word Sense Disambiguation

Author: PanZhaoZhi
Tutor: YaoJianMin
School: Suzhou University
Course: Applied Computer Technology
Keywords: The supervised way Word sense disambiguation Machine learning method Combination of multiple classifiers Bootstrapping
CLC: TP391.1
Type: Master's thesis
Year: 2009
Downloads: 32
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Abstract


Word sense disambiguation (WSD) has very important theoretical and practical significance for many natural language processing applications and is often assumed to be an intermediate task, which is essential for applications such as machine translation, information retrieval, content and thematic analysis, and even grammatical analysis. Currently, two main tendencies can be found in this research area: knowledge-based methods and corpus-based methods. The first one rely on previously acquired linguistic knowledge, and the second ones use techniques from statistics and machine learning to induce models of language usage from large samples of that is, the corpus is previously tagged with correct answers or not.This paper uses supervised way to solve word sense disambiguation. We use a variety of machine learning methods extract different information to build many classifiers. After analyzing the performance of these classifiers, the outputs are combined and using different algorithms to build multiple classifiers. The result of experiments shows that the multiple classifier system outperforms individual classifier. In the past four matches for wsd, the supervised way always gains the best performance.The supervised way has the advantage that it gains high accuracy, but it needs the corpus tagged with correct answers. So the traditional supervised way is limited to several words. This paper makes use of bootstrapping, and automatically gets sentences from the Internet. After dealing with these sentences, a tagged corpus is formed. So the supervised way can be used widely.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Text Processing
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