Measuring Sentences Similarity: A Survey
It provides a comprehensive overview for researchers and practitioners in NLP, but is incremental as it synthesizes existing methods without introducing new techniques.
This survey reviews approaches for measuring sentence similarity, a crucial task for NLP applications like text classification and information retrieval, and finds that combining multiple perspectives yields better results.
This study is to review the approaches used for measuring sentences similarity. Measuring similarity between natural language sentences is a crucial task for many Natural Language Processing applications such as text classification, information retrieval, question answering, and plagiarism detection. This survey classifies approaches of calculating sentences similarity based on the adopted methodology into three categories. Word-to-word based, structure based, and vector-based are the most widely used approaches to find sentences similarity. Each approach measures relatedness between short texts based on a specific perspective. In addition, datasets that are mostly used as benchmarks for evaluating techniques in this field are introduced to provide a complete view on this issue. The approaches that combine more than one perspective give better results. Moreover, structure based similarity that measures similarity between sentences structures needs more investigation.