A Convolutional Neural Network in Legal Question Answering
نویسندگان
چکیده
Our legal question answering system combines legal information retrieval and textual entailment, and we propose a legal question answering system that exploits a deep convolutional neural network. We have evaluated our system using the training data from the competition on legal information extraction/entailment (COLIEE). The competition focuses on the legal information processing related to answering yes/no questions from Japanese legal bar exams, and it consists of two phases: legal ad-hoc information retrieval, and textual entailment. Phase 1 requires the identification of Japan civil law articles relevant to a legal bar exam query. For that phase, we have implemented TF-IDF and Ranking SVM. Phase 2 is to answer “Yes” or “No” to previously unseen queries, by comparing the meanings of queries with relevant articles. Our choice of features used for Phase 2 focuses on word embeddings, syntactic similarities and identification of negation/antonym relations. Phase 2 is a textual entailment problem, and we use a convolutional neural network with dropout regularization and Rectified Linear Units. To our knowledge, our study is the first approach adopting deep learning in the textual entailment field. Experimental evaluation demonstrates the effectiveness of the convolutional neural network and dropout regularization. The results show that our deep learning-based method outperforms the SVM-based supervised model.
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