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Legal Document Retrieval Using Document Vector Embeddings and Deep Learning

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Book cover Intelligent Computing (SAI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 857))

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Abstract

Domain specific information retrieval process has been a prominent and ongoing research in the field of natural language processing. Many researchers have incorporated different techniques to overcome the technical and domain specificity and provide a mature model for various domains of interest. The main bottleneck in these studies is the heavy coupling of domain experts, that makes the entire process to be time consuming and cumbersome. In this study, we have developed three novel models which are compared against a golden standard generated via the on line repositories provided, specifically for the legal domain. The three different models incorporated vector space representations of the legal domain, where document vector generation was done in two different mechanisms and as an ensemble of the above two. This study contains the research being carried out in the process of representing legal case documents into different vector spaces, whilst incorporating semantic word measures and natural language processing techniques. The ensemble model built in this study, shows a significantly higher accuracy level, which indeed proves the need for incorporation of domain specific semantic similarity measures into the information retrieval process. This study also shows, the impact of varying distribution of the word similarity measures, against varying document vector dimensions, which can lead to improvements in the process of legal information retrieval.

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Notes

  1. 1.

    https://www.westlaw.com/.

  2. 2.

    https://www.lexisnexis.com/.

  3. 3.

    https://www.westlaw.com/.

  4. 4.

    https://www.lexisnexis.com/.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Moratuwa, Moratuwa, Sri Lanka

    Keet Sugathadasa, Buddhi Ayesha, Nisansa de Silva, Amal Shehan Perera, Vindula Jayawardana & Dimuthu Lakmal

  2. University of London International Programmes, University of London, London, UK

    Madhavi Perera

Authors
  1. Keet Sugathadasa
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  2. Buddhi Ayesha
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  3. Nisansa de Silva
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  4. Amal Shehan Perera
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  5. Vindula Jayawardana
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  6. Dimuthu Lakmal
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  7. Madhavi Perera
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Corresponding author

Correspondence to Keet Sugathadasa .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Department of Information Science, Saga University, Honjo, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Sugathadasa, K. et al. (2019). Legal Document Retrieval Using Document Vector Embeddings and Deep Learning. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 857. Springer, Cham. https://doi.org/10.1007/978-3-030-01177-2_12

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