Joint entity and relation extraction with part-of-speech-aware attention and dependency parsing embedding

Joint entity and relation extraction is an important task in natural language processing, whose purpose is to obtain all triples in text. However, the existing models seldom pay attention to the part-of-speech (pos) of each word and the dependency parsing (dp) in the sentence. To solve these problems. a joint extraction model with part-of-speech-aware attention and dependency parsing embedding is proposed, named PADPE. The proposed model obtains better word representation through pos-aware attention mechanism. In addition, the parts of speech and dependency characteristics are integrated respectively in entity classification and relation classification to improve the accuracy of the classifier. The experimental results demonstrate that our model can solve the overlapping triple problem more effectively and outperform other baselines on three public datasets.