| Summary: | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mfenced separators="" open="⟨" close="⟩"><mi>S</mi><mo>,</mo><mi>R</mi></mfenced></semantics></math></inline-formula> Extraction, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mfenced separators="" open="⟨" close="⟩"><mi>R</mi><mo>,</mo><mi>O</mi></mfenced></semantics></math></inline-formula> Extraction and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mfenced separators="" open="⟨" close="⟩"><mi>S</mi><mo>,</mo><mi>R</mi><mo>,</mo><mi>O</mi></mfenced></semantics></math></inline-formula> 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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