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Spectral Text Similarity Measures

  • Conference paper
  • First Online:
Computational Linguistics and Intelligent Text Processing (CICLing 2019)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13452))

  • 359 Accesses

Abstract

Estimating semantic similarity between texts is of vital importance in many areas of natural language processing like information retrieval, question answering, text reuse, or plagiarism detection.

Prevalent semantic similarity estimates based on word embeddings are noise sensitive. Thus, small individual term similarities can have in aggregate a considerable influence on the total estimation value. In contrast, the methods proposed here exploit the spectrum of the product of embedding matrices, which leads to increased robustness when compared with conventional methods.

We apply these estimate on two tasks, which are the assignment of people to the best matching marketing target group and finding the correct match between sentences belonging to two independent translations of the same novel. The evaluation revealed that our proposed method based on the spectral norm could increase the accuracy compared to several baseline methods in both scenarios.

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Notes

  1. 1.

    This corpus can be obtained under the URL https://www.researchgate.net/publication/332072718_alignmentPurloinedLettertar.

  2. 2.

    http://www.20min.ch.

  3. 3.

    https://github.com/faraday/wikiprep-esa.

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Acknowledgement

Hereby we thank the Jaywalker GmbH as well as the Jaywalker Digital AG for their support regarding this publication and especially for annotating the contest data with the best-fitting youth milieus.

Author information

Authors and Affiliations

  1. School of Computer Science and Information Technology, Lucerne University of Applied Sciences and Arts, Lucerne, Switzerland

    Tim vor der Brück & Marc Pouly

Authors
  1. Tim vor der Brück
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  2. Marc Pouly
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Corresponding authors

Correspondence to Tim vor der Brück or Marc Pouly .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

A Example Contest Answer

A Example Contest Answer

The following snippet is an example user answer for the travel contest (contest 1):

  1. 1.

    Jordanien: Ritt durch die Wüste und Petra im Morgengrauen bestaunen bevor die Touristenbusse kommen

  2. 2.

    Cook Island: Schnorcheln mit Walhaien und die Seele baumeln lassen

  3. 3.

    USA: Eine abgespaceste Woche am Burning Man Festival erleben

English translation:

  1. 1.

    Jordan: Ride through the desert and marveling Petra during sunrise before the arrival of tourist buses

  2. 2.

    Cook Island: Snorkeling with whale sharks and relaxing

  3. 3.

    USA: Experience an awesome week at the Burning Man Festival

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vor der Brück, T., Pouly, M. (2023). Spectral Text Similarity Measures. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13452. Springer, Cham. https://doi.org/10.1007/978-3-031-24340-0_7

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  • DOI: https://doi.org/10.1007/978-3-031-24340-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24339-4

  • Online ISBN: 978-3-031-24340-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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