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nigam@COLIEE-22: Legal Case Retrieval and Entailment Using Cascading of Lexical and Semantic-Based Models

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New Frontiers in Artificial Intelligence (JSAI-isAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13859))

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Abstract

This paper describes our submission to the Competition on Legal Information Extraction/Entailment 2022 (COLIEE-2022) workshop on case law competition for tasks 1 and 2. Task 1 is a legal case retrieval task, which involves reading a new case and extracting supporting cases from the provided case law corpus to support the decision. Task 2 is a legal case entailment task, involving the identification of a paragraph from existing cases that entails the decision in a relevant case. We employed the neural models Sentence-BERT and Sent2Vec for semantic understanding and the traditional information retrieval model BM25 for exact matching in both tasks. As a result, our team (named “nigam”) ranked 5th among all the teams in Tasks 1 and 2. Experimental results indicate that the traditional information retrieval model BM25 still outperforms neural network-based models.

S. K. Nigam and N. Goel—These authors contributed equally to this work.

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Notes

  1. 1.

    https://github.com/ShubhamKumarNigam/COLIEE-22.

  2. 2.

    https://spacy.io/usage/linguistic-features#sbd.

  3. 3.

    https://en.wikipedia.org/wiki/Okapi_BM25.

  4. 4.

    https://github.com/dorianbrown/rank_bm25.

  5. 5.

    https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html.

  6. 6.

    https://github.com/epfml/sent2vec.

  7. 7.

    https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html.

  8. 8.

    https://www.sbert.net/.

  9. 9.

    https://www.sbert.net/docs/pretrained_models.html.

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Author information

Authors and Affiliations

  1. Indian Institute of Technology, Kanpur, Kanpur, India

    Shubham Kumar Nigam & Arnab Bhattacharya

  2. Vellore Institute of Technology, Chennai, India

    Navansh Goel

Authors
  1. Shubham Kumar Nigam
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  2. Navansh Goel
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  3. Arnab Bhattacharya
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Corresponding author

Correspondence to Shubham Kumar Nigam .

Editor information

Editors and Affiliations

  1. Tokyo Metropolitan University, Tokyo, Japan

    Yasufumi Takama

  2. Kansai University, Osaka, Japan

    Katsutoshi Yada

  3. National Institute of Informatics, Tokyo, Japan

    Ken Satoh

  4. Chiba University, Chiba, Japan

    Sachiyo Arai

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Nigam, S.K., Goel, N., Bhattacharya, A. (2023). nigam@COLIEE-22: Legal Case Retrieval and Entailment Using Cascading of Lexical and Semantic-Based Models. In: Takama, Y., Yada, K., Satoh, K., Arai, S. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2022. Lecture Notes in Computer Science(), vol 13859. Springer, Cham. https://doi.org/10.1007/978-3-031-29168-5_7

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  • DOI: https://doi.org/10.1007/978-3-031-29168-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-29167-8

  • Online ISBN: 978-3-031-29168-5

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