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Non-Contextual vs Contextual Word Embeddings in Multiword Expressions Detection

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Computational Collective Intelligence (ICCCI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13501))

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Abstract

Multiword Expression (MWE) detection is a crucial problem for many NLP applications. Recent methods approach it as a sequence labeling task and require manually annotated corpus. Traditional methods are based on statistical association measures and express limited accuracy, especially on smaller corpora. In this paper, we propose a novel weakly supervised method for extracting MWEs which concentrates on differences between interactions with context between the whole MWE and its component words. The interactions are represented by contextual embeddings (neural language models) and the observations are collected from various occurrence contexts of both the whole MWEs and their single word components. Our method uses a MWE lexicon as the sole knowledge base, and extracts training samples by matching the lexicon against a corpus to build classifiers for MWE recognition by Machine Learning. Thus, our approach does not require a corpus annotated with MWE occurrences, and also works with a limited corpus and a MWE list (\(\approx \)1400 MWEs in this work). It uses a general contextual embeddings model, HerBERTa, a kind of BERT model for Polish. The proposed method was evaluated on the Polish part of the PARSEME corpus and expressed very significant gain in comparison to the top methods from the PARSEME competition. The proposed method can be quite easily applied to other languages.

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Notes

  1. 1.

    Such MWEs form the vast majority of cases, both in PARSEME (only 111 out of 1,481 total correct MWE are longer) and plWordNet.

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Acknowledgements

This work was partially supported by the National Science Centre, Poland, project no. 2019/33/B/HS2/02814; the statutory funds of the Department of Computational Intelligence, Wroclaw University of Science and Technology; the Polish Ministry of Education and Science, CLARIN-PL Project.

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Authors and Affiliations

  1. Department of Artificial Intelligence, Wrocław University of Science and Technology, Wrocław, Poland

    Maciej Piasecki & Kamil Kanclerz

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  1. Maciej Piasecki
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  2. Kamil Kanclerz
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Correspondence to Maciej Piasecki .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  3. University of Pau and Pays de l'Adour, Anglet, France

    Richard Chbeir

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Adrianna Kozierkiewicz

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Piasecki, M., Kanclerz, K. (2022). Non-Contextual vs Contextual Word Embeddings in Multiword Expressions Detection. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2022. Lecture Notes in Computer Science(), vol 13501. Springer, Cham. https://doi.org/10.1007/978-3-031-16014-1_16

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  • DOI: https://doi.org/10.1007/978-3-031-16014-1_16

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