A Knowledge-Enriched and Span-Based Network for Joint Entity and Relation Extraction

The joint extraction of entities and their relations from certain texts plays a significant role in most natural language processes. For entity and relation extraction in a specific domain, we propose a hybrid neural framework consisting of two parts: a span-based model and a graph-based model. The span-based model can tackle overlapping problems compared with BILOU methods, whereas the graph-based model treats relation prediction as graph classification. Our main contribution is to incorporate external lexical and syntactic knowledge of a specific domain, such as domain dictionaries and dependency structures from texts, into end-to-end neural models. We conducted extensive experiments on a Chinese military entity and relation extraction corpus. The results show that the proposed framework outperforms the baselines with better performance in terms of entity and relation prediction. The proposed method provides insight into problems with the joint extraction of entities and their relations.

Automated summary

The joint extraction of entities and their relations from texts is crucial for natural language processing. A hybrid neural framework incorporating external knowledge improved performance in handling overlapping issues and relation prediction. Experiments on a Chinese military corpus validated the effectiveness of the proposed method in entity and relation extraction.