Unsupervised Identification of Relevant Cases & Statutes Using Word Embeddings

Published in Forum for Information Retrieval Evaluation, Kolkata, 2019

Recommended citation: S. Mandal and S.D. Das. Unsupervised Identification of Relevant Cases & Statutes Using Word Embeddings . Forum for Information Retrieval Evaluation, Kolkata (2019). http://ceur-ws.org/Vol-2517/T1-5.pdf

abstact
In this paper, we have described the systems that we submitted as team JU SRM for FIRE 2019 track on Artificial Intelligence for Legal Assistance (AILA 2019). The two tasks in this track were 1) identifying relevant prior cases and 2) identifying relevant statutes. For both of these tasks, we took an unsupervised approach using pre-trained wordembeddings for encoding texts and calculating relevance using cosinesimilarity between the query and target documents.

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@article{mandal2019unsupervised, title={Unsupervised identification of relevant cases \& statutes using word embeddings}, author={Mandal, Soumil and Das, Sourya Dipta}, journal={Proceedings of FIRE}, year={2019} }