Building Legal Case Retrieval Systems with Lexical Matching and Summarization using A Pre-Trained Phrase Scoring Model

Abstract

We present our method for tackling the legal case retrieval task of the Competition on Legal Information Extraction/Entailment 2019. Our approach is based on the idea that summarization is important for retrieval. On one hand, we adopt a summarization based model called encoded summarization which encodes a given document into continuous vector space which embeds the summary properties of the document. We utilize the resource of COLIEE 2018 on which we train the document representation model. On the other hand, we extract lexical features on different parts of a given query and its candidates. We observe that by comparing different parts of the query and its candidates, we can achieve better performance. Furthermore, the combination of the lexical features with latent features by the summarization-based method achieves even better performance. We have achieved the state-of-the-art result for the task on the benchmark of the competition.

Supporting Complaints Investigation for Nursing and Midwifery Regulatory Agencies

“…Explainability is a highly desirable feature for decision support systems, especially in healthcare applications. Different types of information can be presented to users as explanations, including attention distributions (Mullenbach et al, 2018;Feng et al, 2020), similar past cases (Agirre et al, 2012;Rus et al, 2013;Cui et al, 2017;Tran et al, 2019), and salient words in the input text (Ribeiro et al, 2016;Lundberg and Lee, 2017). In the legal domain, to justify a verdict, relevant items in the law are often provided as explanations (Rabelo et al, 2020;Shaffer and Mayhew, 2019).…”

Searching for Legal Documents at Paragraph Level: Automating Label Generation and Use of an Extended Attention Mask for Boosting Neural Models of Semantic Similarity

“…Recently, data-driven neural approaches have emerged as promising components of a new generation of increasingly automatic systems that may require less human interference, curation or updating effort (Tran et al 2020, Zhong et al 2020. While this trend is in its early stages, its maturation could help to deal with some of the above-mentioned limitations.…”

“…Such a retrieval system may not only help expert users with deep expertise in the domain but can also benefit non-expert users. The latter can, for example, try to develop a preliminary understanding of a situation, when selecting or approaching a lawyer, to better navigate the justice system (Bhattacharya et al 2019, Boniol et al 2020, Chen et al 2013, Tran et al 2020. Both expert and non-expert users may then be interested in a) identifying where the legal problem fits in regarding legal concepts, b) what legal actions are potentially relevant, and c) what were the outcomes of similar cases (Bhattacharya et al 2019, Hafner 1980, Zhong et al 2020.…”

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

“…Explainability is a highly desirable feature for decision support systems, especially in healthcare applications. Different types of information can be presented to users as explanations, including attention distributions (Mullenbach et al, 2018;, similar past cases (Agirre et al, 2012;Rus et al, 2013;Cui et al, 2017;Tran et al, 2019), and salient words in the input text Lundberg and Lee, 2017). In the legal domain, to justify a verdict, relevant items in the law are often provided as explanations (Rabelo et al, 2020;Shaffer and Mayhew, 2019).…”