A joint extraction model of entities and relations based on relation decomposition

Extracting entities and relations from unstructured text is an essential task in the field of information extraction. Existing work mainly pipeline extraction and joint decoding methods. However, these methods are unable to extract overlapping entities and relations, and ignore the task correlation between entity and relation extraction. In this paper, we first introduce the BERT pre-training model to model the text more finely. Then, we decompose the extraction into relation extraction and entity recognition. Relation extraction is transformed into a relation classification task. Entity recognition is transformed into a sequence labeling task. The recognition entity includes a head entity and a tail entity. We evaluate the model on the New York Times (NYT) and WebNLG datasets. Compared with most existing models, excellent results have been obtained. Experimental results show that our model can fully capture the semantic interdependence between the two tasks of entity and relation extraction, reduce the interference of unrelated entity pairs, and effectively solve the problem of entity overlap.