Combining Statistical Machine Learning with Transformation Rule Learning for Vietnamese Word Sense Disambiguation

Phu-Hung Dinh, Ngoc-Khuong Nguyen, Anh-Cuong Le
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引用次数: 4

Abstract

Word Sense Disambiguation (WSD) is the task of determining the right sense of a word depending on the context it appears. Among various approaches developed for this task, statistical machine learning methods have been showing their advantages in comparison with others. However, there are some cases which cannot be solved by a general statistical model. This paper proposes a novel framework, in which we use the rules generated by transformation based learning (TBL) to improve the performance of a statistical machine learning model. This framework can be considered as a combination of a rule-based method and statistical based method. We have developed this method for the problem of Vietnamese WSD and achieved some promising results.
结合统计机器学习和转换规则学习的越南语词义消歧研究
词义消歧(WSD)是根据出现的上下文确定单词的正确含义的任务。在为这项任务开发的各种方法中,统计机器学习方法已经显示出与其他方法相比的优势。然而,有些情况是一般统计模型无法解决的。本文提出了一个新的框架,在该框架中,我们使用基于转换的学习(TBL)生成的规则来提高统计机器学习模型的性能。这个框架可以看作是基于规则的方法和基于统计的方法的结合。我们针对越南水务问题开发了这种方法,并取得了一些令人满意的结果。
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