This paper aims to fully present a new word sense disambiguation method that has been introduced in Hristea and Popescu (Fundam Inform 91(3–4):547–562, 2009) and so far tested in the case of adjectives (Hristea and Popescu in Fundam Inform 91(3–4):547–562, 2009) and verbs (Hristea in Int Rev Comput Softw 4(1):58–67, 2009). We hereby extend the method to the case of nouns and draw conclusions regarding its performance with respect to all these parts of speech. The method lies at the border between unsupervised and knowledge-based techniques. It performs unsupervised word sense disambiguation based on an underlying Naïve Bayes model, while using WordNet as knowledge source for feature selection. The performance of the method is compared to that of previous approaches that rely on completely different feature sets. Test results for all involved parts of speech show that feature selection using a knowledge source of type WordNet is more effective in disambiguation than local type features (like part-of-speech tags) are.
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机译:本文旨在全面介绍一种新的单词义消歧方法,该方法已在Hristea和Popescu中引入(Fundam Inform 91(3–4):547-562,2009),并且到目前为止已在形容词(Hristea和Popescu)中进行了测试。 Fundam Inform 91(3-4):547-562,2009)和动词(Hristea in Int Rev Comput Softw 4(1):58-67,2009)。我们在此将方法扩展到名词的情况,并就其在所有这些词性方面的表现得出结论。该方法位于无监督和基于知识的技术之间的边界。它基于基础的朴素贝叶斯模型执行无监督的词义消歧,同时使用WordNet作为特征选择的知识源。将该方法的性能与依赖完全不同的功能集的先前方法的性能进行了比较。对所有涉及的语音部分的测试结果表明,使用WordNet类型的知识源进行的特征选择在歧义消除方面比局部类型的特征(如词性标记)更有效。
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