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Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement

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  • Yang‐Yin Lee
  • Hao Ke
  • Ting‐Yu Yen
  • Hen‐Hsen Huang
  • Hsin‐Hsi Chen

Abstract

In this research, we propose 3 different approaches to measure the semantic relatedness between 2 words: (i) boost the performance of GloVe word embedding model via removing or transforming abnormal dimensions; (ii) linearly combine the information extracted from WordNet and word embeddings; and (iii) utilize word embedding and 12 linguistic information extracted from WordNet as features for Support Vector Regression. We conducted our experiments on 8 benchmark data sets, and computed Spearman correlations between the outputs of our methods and the ground truth. We report our results together with 3 state‐of‐the‐art approaches. The experimental results show that our method can outperform state‐of‐the‐art approaches in all the selected English benchmark data sets.

Suggested Citation

  • Yang‐Yin Lee & Hao Ke & Ting‐Yu Yen & Hen‐Hsen Huang & Hsin‐Hsi Chen, 2020. "Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(6), pages 657-670, June.
    • Handle: RePEc:bla:jinfst:v:71:y:2020:i:6:p:657-670
    DOI: 10.1002/asi.24289
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    References listed on IDEAS

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    1. Roy Rada & Ellen Bicknell, 1989. "Ranking documents with a thesaurus," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 40(5), pages 304-310, September.
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    Cited by:

    1. Chen, Hongshu & Jin, Qianqian & Wang, Ximeng & Xiong, Fei, 2022. "Profiling academic-industrial collaborations in bibliometric-enhanced topic networks: A case study on digitalization research," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    2. Qianqian Jin & Hongshu Chen & Ximeng Wang & Tingting Ma & Fei Xiong, 2022. "Exploring funding patterns with word embedding-enhanced organization–topic networks: a case study on big data," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5415-5440, September.

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