Sentence Bag Graph Formulation for Biomedical Distant Supervision Relation Extraction
This work addresses a key challenge in biomedical relation extraction, offering a novel solution that enhances accuracy in a domain-critical for research and applications, though it is incremental in its graph-based approach.
The paper tackles noisy labeling and inter-sentence dependencies in distantly-supervised relation extraction by proposing a graph-based framework that aggregates information across sentence bags, achieving significant performance improvements over state-of-the-art methods on biomedical and general datasets.
We introduce a novel graph-based framework for alleviating key challenges in distantly-supervised relation extraction and demonstrate its effectiveness in the challenging and important domain of biomedical data. Specifically, we propose a graph view of sentence bags referring to an entity pair, which enables message-passing based aggregation of information related to the entity pair over the sentence bag. The proposed framework alleviates the common problem of noisy labeling in distantly supervised relation extraction and also effectively incorporates inter-dependencies between sentences within a bag. Extensive experiments on two large-scale biomedical relation datasets and the widely utilized NYT dataset demonstrate that our proposed framework significantly outperforms the state-of-the-art methods for biomedical distant supervision relation extraction while also providing excellent performance for relation extraction in the general text mining domain.