A Relation-Oriented Approach for Complex Entity Relation Extraction

Entity relation extraction targets the extraction of structured triples from unstructured text, and is the start of the entire knowledge graph lifecycle. In recent advances, Machine Reading Comprehension (MRC) based approaches provide new paradigms for entity relationship extraction and achieve the state-of-the-art performance. Aiming at the features of nested entities, overlapping relationships and distant entities in recipe texts, this study proposes a relation-oriented approach for complex entity relation extraction. This approach addresses the entity redundancy problem caused by the traditional pipeline models which are entity-first methods. By predicting the starting and ending of entities, it solves the problem of nested entities that cannot be identified by traditional sequence labeling methods. Finally, the error propagation issue is mitigated by the triple determination module. We conduct extensive experiments on multi-datasets in both English and Chinese, and the experimental results show that our method significantly outperforms the baseline model in terms of both precision, recall and micro-F1 score.