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Find Relevant Cases in All Cases: Your Journey at Doctrine

Published:18 July 2019Publication History

ABSTRACT

Domain-specific Information Retrieval (IR) is generally challenging because of the rare datasets or benchmarks, niche vocabularies and more limited literature coverage. Legal IR is no exception and presents other obstacles, reinforcing the need for innovation and, sometimes, paradigm shifts. Doctrine, one of the largest Legaltech companies in Europe, dedicates an entire data science team to advance on these problems and identify new opportunities. In this presentation, we provide some intuition regarding the specificities of legal IR (e.g., what is relevance?), and we introduce some of the solutions currently used on doctrine.fr.

Particularly, we show how we use named entity recognition in the various forms of contents we host, and how it enhances the search engine. With knowledge extracted from documents, we may built large enough datasets and train learning-to-rank algorithms. This, combined with several specific-domain vocabulary enrichments to increase recall, dramatically improves the search experience for our users.

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  1. Find Relevant Cases in All Cases: Your Journey at Doctrine

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        • Published in

          cover image ACM Conferences
          SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
          July 2019
          1512 pages
          ISBN:9781450361729
          DOI:10.1145/3331184

          Copyright © 2019 Owner/Author

          Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 18 July 2019

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          Acceptance Rates

          SIGIR'19 Paper Acceptance Rate84of426submissions,20%Overall Acceptance Rate792of3,983submissions,20%
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