Improving Distantly-Supervised Relation Extraction through Label Prompt

Distantly supervised relation extraction (DSRE) aims to automatically identify relation facts from unstructured text. Most current DSRE works solve the noise problem based on the bag-level, but the denoising ability of these methods decreases when the bag consists of fewer sentences. In this study, we propose a Distantly supervised Relation extraction with Label Prompt (DRLP) framework. We use textual labels (such as label names) as label prompts to alleviate the problem of decreased denoising ability by utilizing the information of entities and relations in label names. During the training process, label prompts are directly connected to the sentences in the bag to provide a more comprehensive bag representation, and label prompts are randomly deleted based on the number of sentences in the bag. Moreover, we design a residual selective attention mechanism that minimizes the influence of spurious features and optimizes the utilization of label information. Our framework is evaluated on NYT-10d and NYT-10m, the results indicate that our method outperforms the state-of-the-art methods.