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Document-level Relation Extraction with Two-stage Dynamic Graph Attention Networks by Qi Sun; Kun Zhang; Kun Huang; Tiancheng Xu; Xun Li; Yaodi Liu is a Computer Science article available to read on EtoBox.

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Document-level Relation Extraction (RE) aims to infer complex semantic relations between entities in a document. Previous approaches leverage a multi-classification model to predict relation types between each entity pair. However, in contrast to sentence-level RE, document-level RE contains various entities expressed by mentions appearing across multiple sentences in a document. Therefore, the amount of negative instances (‘no relationship’) significantly outnumbers that of other positive instances in document-level RE. In addition, most existing methods construct static graphs with heuristic rules to capture the interactions among entities. However, these heuristic rules ignore the specificities of the documents. In this study, we propose a novel two-stage framework to extract document-level relations based on dynamic graph attention networks, namely TDGAT. In the first stage, we capture the relational links of the entity pairs using a binary classification model. In the second stage, we extract fine-grained relations among entities, including the type of ‘NA (no relationship)’. To reduce error propagation, we regard the entity pair links predicted in the first stage as the prior

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Author

Qi Sun; Kun Zhang; Kun Huang; Tiancheng Xu; Xun Li; Yaodi Liu

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)