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Easing Legal News Monitoring with Learning to Rank and BERT

Sanchez, L; He, J; Manotumruksa, J; Albakour, D; Martinez, M; Lipani, A; (2020) Easing Legal News Monitoring with Learning to Rank and BERT. In: Proceedings of the European Conference on Information Retrieval ECIR 2020: Advances in Information Retrieval. (pp. pp. 336-343). Springer, Cham: Cham, Switzerland. Green open access

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Abstract

While ranking approaches have made rapid advances in the Web search, systems that cater to the complex information needs in professional search tasks are not widely developed, common issues and solutions typically rely on dedicated search strategies backed by ad-hoc retrieval models. In this paper we present a legal search problem where professionals monitor news articles with constant queries on a periodic basis. Firstly, we demonstrate the effectiveness of using traditional retrieval models against the Boolean search of documents in chronological order. In an attempt to capture the complex information needs of users, a learning to rank approach is adopted with user specified relevance criteria as features. This approach, however, only achieves mediocre results compared to the traditional models. However, we find that by fine-tuning a contextualised language model (e.g. BERT), significantly improved retrieval performance can be achieved, providing a flexible solution to satisfying complex information needs without explicit feature engineering.

Type: Proceedings paper
Title: Easing Legal News Monitoring with Learning to Rank and BERT
Event: The European Conference in Information Retrieval (ECIR)
Location: Lisbon, Portugal
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-45442-5_42
Publisher version: https://doi.org/10.1007/978-3-030-45442-5_42
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Professional search Complex information needs BERT
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10090451
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