In this paper, we introduce a novel knowledge-based word-sense disambiguation (WSD) system. In particular, the main goal of our research is to find an effective way to filter out unnecessary information by using word similarity. For this, we adopt two methods in our WSD system. First, we propose a novel encoding method for word vector representation by considering the graphical semantic relationships from the lexical knowledge bases, and the word vector representation is utilized to determine the word similarity in our WSD system. Second, we present an effective method for extracting the contextual words from a text for analyzing an ambiguous word based on word similarity. The results demonstrate that the suggested methods significantly enhance the baseline WSD performance in all corpora. In particular, the performance on nouns is similar to those of the state-of-the-art knowledge-based WSD models, and the performance on verbs surpasses that of the existing knowledge-based WSD models.

在本文中，我们介绍了一种新颖的基于知识的词义消歧（WSD）系统。特别地，我们研究的主要目标是找到一种有效的方法，通过使用单词相似性来过滤掉不必要的信息。为此，我们在WSD系统中采用了两种方法。首先，我们考虑词法知识库中的图形语义关系，提出一种新颖的词向量表示编码方法，并利用词向量表示来确定我们WSD系统中的词相似度。其次，我们提出一种有效的方法，该方法可从文本中提取上下文词，以基于词相似度来分析歧义词。结果表明，所建议的方法显着提高了所有语料库中基线WSD的性能。特别是，