The Evaluation of Sentence Similarity Measures

  • Authors:

  • Palakorn Achananuparp;Xiaohua Hu;Xiajiong Shen

  • Affiliations:

  • College of Information Science and Technology, Drexel University, Philadelphia, PA 19104;College of Information Science and Technology, Drexel University, Philadelphia, PA 19104 and College of Computer and Information Engineering, Hehan University, Henan, China;College of Computer and Information Engineering, Hehan University, Henan, China

  • Venue:
  • DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
  • Year:
  • 2008

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Abstract

The ability to accurately judge the similarity between natural language sentences is critical to the performance of several applications such as text mining, question answering, and text summarization. Given two sentences, an effective similarity measure should be able to determine whether the sentences are semantically equivalent or not, taking into account the variability of natural language expression. That is, the correct similarity judgment should be made even if the sentences do not share similar surface form. In this work, we evaluate fourteen existing text similarity measures which have been used to calculate similarity score between sentences in many text applications. The evaluation is conducted on three different data sets, TREC9 question variants, Microsoft Research paraphrase corpus, and the third recognizing textual entailment data set.