A Comprehensive Comparative Study of Word and Sentence Similarity Measures

Abstract

Sentence similarity is considered the basis of many natural language tasks such as information retrieval, question answering and text summarization. The semantic meaning between compared text fragments is based on the words semantic features and their relationships. This article reviews a set of word and sentence similarity measures and compares them on benchmark datasets. On the studied datasets, results showed that hybrid semantic measures perform better than both knowledge and corpus based measures.

arXiv comment7 pages,4 figures

References in corpus (5)

• Using Information Content to Evaluate Semantic Similarity in a Taxonomy
• Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy
• Using Latent Semantic Analysis to Identify Quality in Use (QU) Indicators from User Reviews
• Measuring Software Quality in Use: State-of-the-Art and Research Challenges
• Building a Pilot Software Quality-in-Use Benchmark Dataset