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Inicio  /  Applied Sciences  /  Vol: 13 Par: 2 (2023)  /  Artículo
ARTÍCULO
TITULO

Span-Based Fine-Grained Entity-Relation Extraction via Sub-Prompts Combination

Ning Yu    
Jianyi Liu and Yu Shi    

Resumen

With the development of information extraction technology, a variety of entity-relation extraction paradigms have been formed. However, approaches guided by these existing paradigms suffer from insufficient information fusion and too coarse extraction granularity, leading to difficulties extracting all triples in a sentence. Moreover, the joint entity-relation extraction model cannot easily adapt to the relation extraction task. Therefore, we need to design more fine-grained and flexible extraction methods. In this paper, we propose a new extraction paradigm based on existing paradigms. Then, based on it, we propose SSPC, a method for Span-based Fine-Grained Entity-Relation Extraction via Sub-Prompts Combination. SSPC first decomposes the task into three sub-tasks, namely ???,??? S , R Extraction, ???,??? R , O Extraction and ???,??,??? S , R , O Classification and then uses prompt tuning to fully integrate entity and relation information in each part. This fine-grained extraction framework makes the model easier to adapt to other similar tasks. We conduct experiments on joint entity-relation extraction and relation extraction, respectively. The experimental results show that our model outperforms previous methods and achieves state-of-the-art results on ADE, TACRED, and TACREV.

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