Legal Question Answering using Ranking SVM and Deep Convolutional Neural Network
This work addresses legal professionals needing automated assistance in information extraction, but it is incremental as it builds on existing methods with minor modifications.
The paper tackled legal information retrieval and question answering by using Ranking SVM with paragraph-level features and integrating statistical features into a Convolutional Neural Network, achieving higher accuracy in the question answering task.
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.