题目：Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement
摘要：In recent years, the plentiful information contained in Chinese legal documents has attracted a great deal of attention because of the large-scale release of the judgment documents on China Judgments Online. It is in great need of enabling machines to understand the semantic infor-mation stored in the documents which are transcribed in the form of natural language. The technique of information extraction provides a way of mining the valuable information im-plied in the unstructured judgment documents. We propose a Legal Triplet Extraction System for drug-related criminal judgment documents. The system extracts the entities and the se-mantic relations jointly and benefits from the proposed legal lexicon feature and multi-task learning framework. Furthermore, we manually annotate a dataset for Named Entity Recogni-tion and Relation Extraction in Chinese legal domain, which contributes to training super-vised triplet extraction models and evaluating the model performance. Our experimental re-sults show that the legal feature introduction and multi-task learning framework are feasible and effective for the Legal Triplet Extraction System. The F1 score of triplet extraction finally reaches 0.836 on the legal dataset.