Search

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 proposed 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. [ABSTRACT FROM AUTHOR]