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A graph-based approach to word sense disambiguation. An unsupervised method based on semantic relatedness

机译：基于图的词义消歧方法。一种基于语义关联的无监督方法

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摘要

Word Sense Disambiguation (WSD) is the task of automatically choosing the correct meaning of a word in a context. Due to the importance of this task, it is considered as one of the most important and challenging problems in the field of computational linguistics and plays a crucial role in various natural language processing (NLP) applications. In this paper, we present an improved version of a recent unsupervised graph-based word sense disambiguation method considered to be one of the states of the art techniques. Using WordNet as our knowledge-base, we introduce a new method of combining similarity metrics that uses higher order relations between words to assign appropriate weights to each edge in the graph. Furthermore, we propose a new approach for selecting the most appropriate sense of the target word that makes use of the in-degree centrality algorithm and senses of the neighbor words. Experimental results on benchmark datasets Senseval-2 and Senseval-3 shows that the proposed model outperforms all other graph-based methods presented in the literature.
机译：词义消歧（WSD）是自动在上下文中选择词的正确含义的任务。由于这项任务的重要性，它被认为是计算语言学领域中最重要和最具挑战性的问题之一，并且在各种自然语言处理（NLP）应用程序中扮演着至关重要的角色。在本文中，我们提出了一种最新的无监督基于图的词义消歧方法的改进版本，该方法被认为是最先进的技术之一。使用WordNet作为我们的知识库，我们引入了一种组合相似性度量的新方法，该方法使用单词之间的高阶关系为图的每个边缘分配适当的权重。此外，我们提出了一种新的方法，该方法利用度内中心算法和相邻词的感觉来选择最合适的目标词的感觉。在基准数据集Senseval-2和Senseval-3上的实验结果表明，所提出的模型优于文献中提出的所有其他基于图的方法。

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