Joint entity and relation extraction model based on rich semantics

Extracting entities and relations from unstructured texts has become an important task in the natural language processing (NLP), especially knowledge graphs (KG). However, relation classification (RC) and named entity recognition (NER) tasks are usually considered separately, which lost a lot of associated contextual information. Therefore, a novel end-to-end method based on the attention mechanism integrating convolutional and recurrent neural networks is proposed for joint entity and relation extraction, which can obtain rich semantics and takes full advantage of the associated information between entities and relations without introducing external complicated features. The convolutional operation is employed to obtain character-level and word-level embeddings which are transferred to the multi-head attention mechanism. Then the multi-head attention mechanism can encode contextual semantics and embeddings to obtain efficient semantic representation. Moreover, the rich semantics are encoded to obtain final tag sequence based on recurrent neural networks. Finally, the experiments are performed on NYT10 and NYT11 benchmarks to demonstrate the proposed method. Compared with the current pipe-lined and joint approaches, the experimental results indicate that the proposed method can obtain state-of-the-art performance in terms of the standard F1-score. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The novel method integrating convolutional and recurrent neural networks with attention mechanism achieves joint entity and relation extraction, encoding rich semantics efficiently and taking full advantage of the associated information between entities and relations. Experimental results demonstrate that the proposed method outperforms current pipe-lined and joint approaches in terms of standard F1-score.