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Short Text Similarity Measurement Based on Coupled Semantic Relation and Strong Classification Features

  • Huifang MaEmail author
  • Wen Liu
  • Zhixin Li
  • Xianghong Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11439)

Abstract

Measuring the similarity between short texts is made difficult by the fact that two texts that are semantically related may not contain any words in common. In this paper, we propose a novel short text similarity measure which aggregates coupled semantic relation (CSR) and strong classification features (SCF) to provide a richer semantic context. On the one hand, CSR considers both intra-relation (i.e. co-occurrence of terms based on the modified weighting strategy) and inter-relation (i.e. dependency of terms via paths that connect linking terms) between a pair of terms. On the other hand, Based on SCF for similarity measure is established based on the idea that the more similar two texts are, the more features of strong classification they share. Finally, we combine the above two techniques to address the semantic sparseness of short text. We carry out extensive experiments on real world short texts. The results demonstrate that our method significantly outperforms baseline methods on several evaluation metrics.

Keywords

Short text Coupled semantic relation Strong classification feature Short text similarity 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Huifang Ma
    • 1
    • 2
    Email author
  • Wen Liu
    • 1
  • Zhixin Li
    • 2
  • Xianghong Lin
    • 1
  1. 1.College of Computer Science and TechnologyNorthwest Normal UniversityLanzhouChina
  2. 2.Guangxi Key Laboratory of Multi-source Information Mining and SecurityGuangxi Normal UniversityGuilinChina

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