# Span-Based Fine-Grained Entity-Relation Extraction via Sub-Prompts Combination
###### Abstract  
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 subtasks, namely \(\langle S,R\rangle\) Extraction, \(\langle R,O\rangle\) Extraction and \(\langle S,R,O\rangle\) 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. joint entity and relation extraction; extraction paradigm; model adaptation; prompt tuning +
Footnote †: journal:  
the relation extraction part. \(P3\) first extracts the relations in the sentence and then transfers the information of the relations to the entity extraction part. Nevertheless, in the first step of extraction, there is a lack of information supplement from relation information to entity extraction or entity information to relation extraction. Take the sentence \"Neutropenia and agranulocytosis are risks known to occur with phenothiazines and clozapine.\" in Figure 1 as an example. \(P1\) and \(P2\) are not guided by any relation information when extracting \"Neutropenia\", \"agranulocytosis\", \"phenothiazines\" and \"clozapine\" in the first step, even if the pre-defined target relation \"Adverse-Effect\" has been given in advance. The \"Adverse-Effect\" means \"The effect was caused by the drug.\". The combination of \"effect\", \"drug\", and \"was caused by\" generates a large amount of information, including sequential information, causal information, and semantic information, which is very crucial for entity extraction. So, there is no reason to ignore this information, and we need to find a way to integrate it into the first step of the extraction process. Another issue is that the difference between relation extraction and joint entity and relation extraction is whether the entities in the sentence have been annotated. Therefore, a flexible ERE approach should ensure that it can quickly adapt to RE. To address the challenges above, we propose a new extraction paradigm, which can be described as \([(S,R)\&(R,O)\rightarrow\langle S,R,O\rangle]\). It can not only extract entities but also extract more fine-grained elements under the guidance of relation: \(\langle S,R\rangle\) and \(\langle R,O\rangle\). As the intermediate extraction result, these elements can form triples through logical combination and specific judgment as the final joint extraction result. To implement it, we propose SSPC, a model for Span-based Fine-Grained Entity-Relation Extraction with Sub-Prompts Combination. We introduce the approach of prompt tuning into the model. A typical prompt consists of a template and a set of label words. This form ingeniously conforms to our idea of integrating entity and relation information as long as we convert the relation information into a prompt. Therefore, we first design a set of prompts before extraction, including the S-R prompt, R-O prompt, and S-R-O prompt, and then begin the extraction process.