An Empirical Study of Classifier Combination Based Word Sense Disambiguation

An Empirical Study of Classifier Combination Based Word Sense Disambiguation
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基于分类词组合的词义消歧实证研究

DOI:
10.1587/transinf.2017edp7090
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发表时间:
2018
影响因子:
0.7
通讯作者:
Huang Heyan
Huang Heyan
中科院分区:
计算机科学4区
文献类型:
--
作者:
Lu Wenpeng;Wu Hao;Jian Ping;Huang Yonggang;Huang Heyan

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词义消歧是通过挖掘歧义词的上下文信息来识别歧义词的正确义项。以往的研究表明,分类器组合是提高WSD性能的有效途径。在本文中,我们系统地回顾了基于分类器组合的WSD方法,包括基于概率的方法和基于投票的方法。在此基础上,提出了一种新的基于WSD的分类器组合方法--动态自适应概率加权投票法。与已有方法相比,新方法既能考虑量词的差异,又能考虑歧义实例。在真实数据集上进行了详尽的实验,结果表明我们的方法比最先进的方法更优越。关键词:词义消歧、分类器组合、概率加权投票法、自适应
Word sense disambiguation (WSD) is to identify the right sense of ambiguous words via mining their context information. Previous studies show that classifier combination is an effective approach to enhance the performance of WSD. In this paper, we systematically review state-of-the-art methods for classifier combination based WSD, including probability-based and voting-based approaches. Furthermore, a new classifier combination based WSD, namely the probability weighted voting method with dynamic self-adaptation, is proposed in this paper. Compared with existing approaches, the new method can take into consideration both the differences of classifiers and ambiguous instances. Exhaustive experiments are performed on a real-world dataset, the results show the superiority of our method over state-of-the-art methods. key words: word sense disambiguation, classifier combination, probability weighted voting method, self-adaptation
DOI: --
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期刊: ArXiv
影响因子: --
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