RIF-Proto: A Few-Shot Relation Extraction for Prototype Networks Based on Relational Information Features

Relation extraction stands as a core task within the realm of information extraction, focusing on identifying the target relations between entities in text. While traditional relation extraction has been extensively researched, certain relations lack sufficient labeled data, rendering traditional models ineffective in practical research. Hence, the research focus in this field has shifted towards few-shot learning methods that leverage limited labeled data for training. Significant progress has been made in few-shot relation extraction, with prototype networks garnering attention from scholars for their outstanding performance in few-shot tasks. Prototype networks utilize the clustering idea to achieve relationship classification simply and efficiently, and their training process is mainly divided into three parts: generation of relationship prototypes, relationship classification, and loss calculation. The majority of current research is centered around leveraging external information to enhance the generation of prototypes and loss functions, but due to the difficulty of obtaining external data and the high complexity of data processing, it greatly increases the time cost and the difficulty of implementation. Given the aforementioned challenges, this paper introduces a method for prototype refinement leveraging relationship similarity among existing data. This approach aims to effectively address the issue of significant deviations in relationship prototypes. Initially, extract relationship vectors from the instances in both the support set and query set, followed by computing the relationship similarity between samples based on these vectors, and finally, this paper constructs a prototype correction module, which utilizes the relationship similarity to assign weights to the samples in the support set, to achieve the purpose of prototype correction. In the experiments against the standard dataset FewRel1.0., we observe that the method substantially improves the experimental results.