NC-DRE: Leveraging Non-entity Clue Information for Document-level Relation Extraction

• URL: http://arxiv.org/abs/2204.00255v1
• Date: Fri, 1 Apr 2022 07:30:26 GMT
• Title: NC-DRE: Leveraging Non-entity Clue Information for Document-level Relation Extraction
• Authors: Liang Zhang, Yidong Cheng
• Abstract summary: Document-level relation extraction (RE) requires reasoning on multiple entities in different sentences to identify complex inter-sentence relations. Previous studies usually employ graph neural networks (GNN) to perform inference upon heterogeneous document-graphs. We propose a novel graph-based model NC-DRE that introduces decoder-to-encoder attention mechanism to leverage Non-entity Clue information for Document-level Relation Extraction.
• Score: 3.276435438007766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Document-level relation extraction (RE), which requires reasoning on multiple entities in different sentences to identify complex inter-sentence relations, is more challenging than sentence-level RE. To extract the complex inter-sentence relations, previous studies usually employ graph neural networks (GNN) to perform inference upon heterogeneous document-graphs. Despite their great successes, these graph-based methods, which normally only consider the words within the mentions in the process of building graphs and reasoning, tend to ignore the non-entity clue words that are not in the mentions but provide important clue information for relation reasoning. To alleviate this problem, we treat graph-based document-level RE models as an encoder-decoder framework, which typically uses a pre-trained language model as the encoder and a GNN model as the decoder, and propose a novel graph-based model NC-DRE that introduces decoder-to-encoder attention mechanism to leverage Non-entity Clue information for Document-level Relation Extraction.

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