论文标题
利用非税项关系来衡量WordNet的语义相似性和相关性
Exploiting Non-Taxonomic Relations for Measuring Semantic Similarity and Relatedness in WordNet
论文作者
论文摘要
计算语言学和人工智能领域中的各种应用都采用语义相似性来解决具有挑战性的任务，例如单词sense disammain，文本分类，信息检索，机器翻译和文档群集。先前关于语义相似性的工作遵循了单一相关方法，主要使用分类学关系“ ISA”。本文探讨了在大型链接数据（例如WordNet知识图）中使用所有类型的非税法关系的好处，以增强现有的语义相似性和相关性度量。我们提出了一种基于新的基于关系的信息内容和基于非税法的加权途径的整体多关系方法，以设计一种全面的语义相似性和相关性度量。为了证明在知识图中利用非税法关系的好处，我们使用了三种策略在不同的粒度层面部署非税法。我们对四个众所周知的黄金标准数据集进行了实验，结果证明了拟议的语义相似性和相关性度量的鲁棒性和可扩展性，这显着改善了现有的相似性度量。
Various applications in the areas of computational linguistics and artificial intelligence employ semantic similarity to solve challenging tasks, such as word sense disambiguation, text classification, information retrieval, machine translation, and document clustering. Previous work on semantic similarity followed a mono-relational approach using mostly the taxonomic relation "ISA". This paper explores the benefits of using all types of non-taxonomic relations in large linked data, such as WordNet knowledge graph, to enhance existing semantic similarity and relatedness measures. We propose a holistic poly-relational approach based on a new relation-based information content and non-taxonomic-based weighted paths to devise a comprehensive semantic similarity and relatedness measure. To demonstrate the benefits of exploiting non-taxonomic relations in a knowledge graph, we used three strategies to deploy non-taxonomic relations at different granularity levels. We conducted experiments on four well-known gold standard datasets, and the results demonstrated the robustness and scalability of the proposed semantic similarity and relatedness measure, which significantly improves existing similarity measures.
