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Improving LocalMaxs Multiword Expression Statistical Extractor

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Computational Science – ICCS 2023 (ICCS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14074))

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Abstract

LocalMaxs algorithm extracts relevant Multiword Expressions from text corpora based on a statistical approach. However, statistical extractors face an increased challenge of obtaining good practical results, compared to linguistic approaches which benefit from language-specific, syntactic and/or semantic, knowledge. First, this paper contributes to an improvement to the LocalMaxs algorithm, based on a more selective evaluation of the cohesion of each Multiword Expressions candidate with respect to its neighbourhood, and a filtering criterion guided by the location of stopwords within each candidate. Secondly, a new language-independent method is presented for the automatic self-identification of stopwords in corpora, requiring no external stopwords lists or linguistic tools. The obtained results for LocalMaxs reach Precision values of about \(80\,\%\) for English, French, German and Portuguese, showing an increase of around \(12-13\,\%\) compared to the previous LocalMaxs version. The performance of the self-identification of stopwords reaches high Precision for top-ranked stopword candidates.

This work is supported by NOVA LINCS (UIDB/04516/2020) with the financial support of FCT.IP.

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Notes

  1. 1.

    \(F1\!=\!\frac{2\,.Precision\times Recall}{Precision+Recall}\).

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Authors and Affiliations

  1. NOVA LINCS, NOVA School of Science and Technology, Costa da Caparica, Portugal

    Joaquim F. Silva & Jose C. Cunha

Authors
  1. Joaquim F. Silva
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  2. Jose C. Cunha
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Corresponding author

Correspondence to Joaquim F. Silva .

Editor information

Editors and Affiliations

  1. Czech Technical University in Prague, Prague, Czech Republic

    Jiří Mikyška

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Silva, J.F., Cunha, J.C. (2023). Improving LocalMaxs Multiword Expression Statistical Extractor. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14074. Springer, Cham. https://doi.org/10.1007/978-3-031-36021-3_13

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  • DOI: https://doi.org/10.1007/978-3-031-36021-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36020-6

  • Online ISBN: 978-3-031-36021-3

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