Comparative analysis of similarity measures for sentence level semantic measurement of text
The accuracy of similarity measurement between sentences is critical to the performance of several applications such as text mining, question answering, and text summarization. This paper focuses on calculating semantic similarities between sentences and performing a comparative analysis among ident...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/10259/1/22.pdf |
| Summary: | The accuracy of similarity measurement between sentences is critical to the performance of several applications such as text mining, question answering, and text summarization. This paper focuses on calculating semantic similarities between sentences and performing a comparative analysis among identified similarity measurement techniques.Comparison between three popular similarity measurements which are Jaccard, Cosine and Dice similarity measures has been conducted.The performance of each identified measurement was evaluated and recorded.In this paper, we use a large lexical database of English known as WordNet to calculate the world-toward semantic similarity.The result of this research concludes that the Jaccard and Dice performs better in measuring the semantic similarity between sentences. |
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