  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.
