Fact triples are a common form of structured knowledge used within the
biomedical domain. As the amount of unstructured scientific texts continues to
grow, manual annotation of these texts for the task of relation extraction
becomes increasingly expensive. Distant supervision offers a viable approach to
combat this by quickly producing large amounts of labeled, but considerably
noisy, data. We aim to reduce such noise by extending an entity-enriched
relation classification BERT model to the problem of multiple instance
learning, and defining a simple data encoding scheme that significantly reduces
noise, reaching state-of-the-art performance for distantly-supervised
biomedical relation extraction. Our approach further encodes knowledge about
the direction of relation triples, allowing for increased focus on relation
learning by reducing noise and alleviating the need for joint learning with
knowledge graph completion.

使用多实例学习和 BERT 模型，我们提出了一个简单的数据编码方案，以降低噪音并获得了远程监控生物医学关系提取的最新性能，同时进一步编码关于关系三元组方向的知识，从而减少噪音并减轻了与知识图完成的联合学习的需要。