Unveiling the relationship between complex networks metrics and word senses
Unveiling the relationship between complex networks metrics and word senses
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DOI：
10.1209/0295-5075/98/18002
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发表时间：
2012-04-01
期刊：
影响因子：
1.8
通讯作者：
Costa, Luciano da F.
中科院分区：
文献类型：
--
作者：
Amancio, Diego R.;Oliveira, Osvaldo N., Jr.;Costa, Luciano da F.
词义的自动消歧（即，对于具有多个含义的词，在给定的上下文中使用哪个含义的识别）对于诸如机器翻译和信息检索的应用是必不可少的，并且代表了开发所谓的语义Web的关键步骤。人类以一种简单的方式消除单词的歧义，但这并不适用于计算机。在本文中，我们解决了词义消歧（WSD）的问题，通过处理文本作为复杂的网络，并表明，词义可以区分周围的歧义词的局部结构特征。我们的目标不是获得最好的消歧系统，但我们发现，在一半的情况下，我们的方法优于传统的浅层方法。我们发现，层次连接和聚类的话通常是最相关的功能为词义消歧。这里报道的结果揭示了复杂网络的语义和结构参数之间的关系。他们还表明，当与传统的技术相结合的复杂网络的方法可能是有用的，以提高大型文本中的意义的歧视。版权所有（C）EPLA，2012
The automatic disambiguation of word senses (i.e., the identification of which of the meanings is used in a given context for a word that has multiple meanings) is essential for such applications as machine translation and information retrieval, and represents a key step for developing the so-called Semantic Web. Humans disambiguate words in a straightforward fashion, but this does not apply to computers. In this paper we address the problem of Word Sense Disambiguation (WSD) by treating texts as complex networks, and show that word senses can be distinguished upon characterizing the local structure around ambiguous words. Our goal was not to obtain the best possible disambiguation system, but we nevertheless found that in half of the cases our approach outperforms traditional shallow methods. We show that the hierarchical connectivity and clustering of words are usually the most relevant features for WSD. The results reported here shed light on the relationship between semantic and structural parameters of complex networks. They also indicate that when combined with traditional techniques the complex network approach may be useful to enhance the discrimination of senses in large texts. Copyright (C) EPLA, 2012