The issue of word sense ambiguity poses a significant challenge in natural
language processing due to the scarcity of annotated data to feed machine
learning models to face the challenge. Therefore, unsupervised word sense
disambiguation methods have been developed to overcome that cha
提出了一种名为 Most Suitable Sense Annotation (MSSA) 的新方法,它通过一种无监督技术来标注每个单词的特定含义,并考虑其上下文的语义效应,从而减轻了自然语言理解中多义性和同音异义词的问题,实现了语义表示方面的三个主要贡献,使用六个不同的基准模型进行 word similarity 测试,结果表明该方法能够产生最先进的结果,胜过了几个更复杂的先进系统。