An End-to-End Entity and Relation Extraction Network with Multi-head Attention

Relation extraction is an important semantic processing task in natural language processing. The state-of-the-art systems usually rely on elaborately designed features, which are usually time-consuming and may lead to poor generalization. Besides, most existing systems adopt pipeline methods, which treat the task as two separated tasks, i.e., named entity recognition and relation extraction. However, the pipeline methods suffer two problems: (1) Pipeline model over-simplifies the task to two independent parts. (2) The errors will be accumulated from named entity recognition to relation extraction. Therefore, we present a novel joint model for entities and relations extraction based on multi-head attention, which avoids the problems in the pipeline methods and reduces the dependence on features engineering. The experimental results show that our model achieves good performance without extra features. Our model reaches an F-score of 85.7% on SemEval-2010 relation extraction task 8, which has competitive performance without extra feature compared with previous joint models. On publication, codes will be made publicly available.