关键词: 关系抽取, 关系分类, 远程监督, 注意力机制, 语义分割

Abstract:

Due to the high complexity of text processing in document-level relation extraction， it is difficult to extract efficient entity relations. Therefore， a path label based document-level extraction method was proposed to select key evidence sentences. Firstly， the Path label was introduced to replace the entity sentence as the processed text dataset for data preprocessing. At the same time， combined with the U-Net model of semantic segmentation， the encoding module at the input end was used to capture the context information of the document entity， and the image style was used to capture the context information of the document entities， and the U-Net semantic segmentation module was used to capture the global dependencies among entity triples. Finally， a Softmax function was introduced to decrease the noise of text extraction. Theoretical analysis and simulation results show that compared with the graph neural network-based RoBERTa （Robustly optimized Bidirectional Encoder Representations from Transformers） （RoBERTa?ATLOP） relation extraction algorithm， Path+U-Net has the F1-score in the development and testing of Document-level Relation Extraction Dataset （DocRED） increased by 1.31 and 0.54 percentage points respectively， and the F1-score in development and testing of Chemical Disease Response （CDR） dataset improved by 1.32 and 1.19 percentage points respectively. At the same time， Path+U-Net has lower extraction cost for datasets and higher extraction accuracy of text， while the correlation between entities is consistent with the correlation in the original dataset. Experimental results show that the proposed extraction algorithm based on path labels can effectively improve the extraction efficiency of long texts.

Key words: relation extraction, relation classification, remote supervision, attention mechanism, semantic segmentation