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Exploiting global context and external knowledge for distantly supervised relation extraction
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AI
DOI：10.1016/j.knosys.2022.110195.png)
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Abstract
En 中文
Distantly supervised relation extraction aims to obtain relational facts from unstructured texts. Although distant supervision can automatically generate labeled training instances, it inevitably suffers from the wrong-label problem. Most of the current work is based on the bag-level for solving the noise problem, where a bag is composed of multiple sentences containing mentions of the same entity pair. However, previous studies mostly represent sentences from a single perspective, wherein insufficient modeling of global information restricts the effectiveness of denoising. In this study, we propose a novel distantly supervised relation extraction approach that incorporates the global contextual information of sentences to guide the denoising process and generate an effective bag -level representation. Simultaneously, knowledge-aware word embeddings were generated to enrich sentence-level representations by introducing both structured knowledge from external knowledge graphs and semantic knowledge from the corpus. The experimental results demonstrate that our pro-posed approach outperforms state-of-the-art methods on both versions of the large-scale benchmark New York Times dataset. In addition, the differences between the two versions of the dataset were investigated through further comparative experiments.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Relation extraction
Distant supervision
Knowledge representation
Word embedding
Gating mechanism
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