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Measuring Directional Semantic Similarity with Multi-features

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Web Technologies and Applications (APWeb 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9931))

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Abstract

Semantic similarity measures between linguistic terms are essential in many Natural Language Processing (NLP) applications. Term similarity is most conventionally perceived as a symmetric relation. However, semantic directional (asymmetric) relations exist in lexical semantics and make symmetric similarity measures less suitable for their identification. Furthermore, directional similarity actually represents even more general conditions and is more practical in some specific NLP applications than symmetric similarity. As the footstone of similarity measures, current semantic features cannot efficiently represent large scale web text collections. Hence, we propose a new directional similarity method, considering feature representations both in linguistic and extra linguistic dimensions. We evaluate our approach on standard word similarity, reporting state-of-the-art performance on multiple datasets. Experiments show that our directional method handles both symmetric and directional semantic relations and leads to clear improvements in entity search and query expansion.

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Notes

  1. 1.

    http://lcl.uniroma1.it/nasari/.

  2. 2.

    http://www.inex.otago.ac.nz/tracks/entityranking/entity-ranking.asp.

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Acknowledgments

This paper is partly supported by the NSFC under grants No. 61433019 and No. 61370104, International Science and Technology Cooperation Program of China under grant No. 2015DFE12860, National 863 Hi-Tech Research and Development Program under grant 2014AA01A301, and Chinese Universities Scientific Fund under grant No. 2015MS077.

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Authors and Affiliations

  1. Services Computing Technology and System Lab, Cluster and Grid Computing Lab, Big Data Technology and System Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Bo Liu, Xuanhua Shi & Hai Jin

Authors
  1. Bo Liu
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  2. Xuanhua Shi
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  3. Hai Jin
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Corresponding author

Correspondence to Hai Jin .

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Editors and Affiliations

  1. School of Computing, University of Utah, Salt Lake City, Utah, USA

    Feifei Li

  2. School of Electrical Engineering, Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. Soochow University , Suzhou, China

    Kai Zheng

  4. Soochow University , Suzhou, China

    Guanfeng Liu

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Liu, B., Shi, X., Jin, H. (2016). Measuring Directional Semantic Similarity with Multi-features. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_44

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  • DOI: https://doi.org/10.1007/978-3-319-45814-4_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45813-7

  • Online ISBN: 978-3-319-45814-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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