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Semantic Textual Similarity as a Service

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Service Research and Innovation (ASSRI 2015, ASSRI 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 234))

Abstract

Ensembling well performing models has been proved to outperform individual models in semantic textual similarity task; however, employing existing models still remains a challenge. In this paper, we tackle this issue by providing a service oriented system to index a text similarity model using RESTful services. We also propose a baseline approach, based on an effective penalty-award weighting schema and word-level edit distance, in which pairs of sentences are divided into two main categories based on the number of substitution, insert, and delete required to convert the first sentence to the second one. It is debated that, when the word-level edit distance is very small, it is wiser to measure dissimilarity than similarity. Using knowledge bases along with common natural language processing tools, the proposed method tries to enhance the accuracy of measuring similarity between two sentences. We compared the proposed method with existing approaches, and we found that it produces promising results. Our source code is freely available on GitLab.

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Notes

  1. 1.

    International Workshop on Semantic Evaluation.

  2. 2.

    https://gitlab.com/mysilver/semantic-text-similarity.

  3. 3.

    It means only word similarity Type 1 to 5 positively contribute to the semantic similarity of two sentences.

  4. 4.

    http://www.rxnlp.com/api-reference/text-similarity-api-reference/.

  5. 5.

    https://dandelion.eu/docs/api/datatxt/sim/v1/.

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Author information

Authors and Affiliations

  1. Payame Noor University, Tehran, Iran

    Roghayeh Fakouri-Kapourchali & Mehdi Khalili

  2. University of New South Wales, Kensington, Australia

    Mohammad-Ali Yaghoub-Zadeh-Fard

Authors
  1. Roghayeh Fakouri-Kapourchali
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  2. Mohammad-Ali Yaghoub-Zadeh-Fard
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  3. Mehdi Khalili
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Corresponding author

Correspondence to Mohammad-Ali Yaghoub-Zadeh-Fard .

Editor information

Editors and Affiliations

  1. Macquarie University, Sydney, New South Wales, Australia

    Amin Beheshti

  2. CSIRO Research Organisation, Dutton Park, Queensland, Australia

    Mustafa Hashmi

  3. Royal Melbourne Institute of Technology, Melbourne, Victoria, Australia

    Hai Dong

  4. Macquarie University, Sydney, New South Wales, Australia

    Wei Emma Zhang

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Fakouri-Kapourchali, R., Yaghoub-Zadeh-Fard, MA., Khalili, M. (2018). Semantic Textual Similarity as a Service. In: Beheshti, A., Hashmi, M., Dong, H., Zhang, W. (eds) Service Research and Innovation. ASSRI ASSRI 2015 2017. Lecture Notes in Business Information Processing, vol 234. Springer, Cham. https://doi.org/10.1007/978-3-319-76587-7_14

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  • DOI: https://doi.org/10.1007/978-3-319-76587-7_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76586-0

  • Online ISBN: 978-3-319-76587-7

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