Legal Question Answering using Ranking SVM and Deep Convolutional Neural Network

Abstract

This paper presents a study of employing Ranking SVM and Convolutional Neural Network for two missions: legal information retrieval and question answering in the Competition on Legal Information Extraction/Entailment. For the first task, our proposed model used a triple of features (LSI, Manhattan, Jaccard), and is based on paragraph level instead of article level as in previous studies. In fact, each single-paragraph article corresponds to a particular paragraph in a huge multiple-paragraph article. For the legal question answering task, additional statistical features from information retrieval task integrated into Convolutional Neural Network contribute to higher accuracy.

15 pages, 2 figures, Tenth International Workshop on Juris-informatics (JURISIN 2016) associated with JSAI International Symposia on AI 2016 (IsAI-2016)

References in corpus (2)

• Deep Learning for Answer Sentence Selection
• Learning to Rank Questions for Community Question Answering with Ranking SVM

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• JEC-QA: A Legal-Domain Question Answering Dataset
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