In this approach, the sentences matched by an entity pair constitute a bag. For this reason these datasets are noisy. Besides that, they are imbalanced, that is, the instances are not evenly distributed across relations.

University of Michigan - Ann Arbor

Neural relation extraction methods can be categorized regarding their assumptions on expressiveness of training instances about the relations:
- Sentence-level relation extraction
- Bag-level relation extraction
- Document-level relation extraction

Neural Relation Extraction Methods

Neural relation extraction: a survey

In this approach, sentence-based annotated training data is used. Annotation contains sentence-triple alignment information, such that sentences in the training set are labeled with the triples. However, insufficient amount of training data is a major drawback as labeled data is not always available in real life scenarios.

Sentence-Level Relation Extraction

Bag-Level Relation Extraction

Sentence-level approach lacks in grasping entity pair relations across a document, that is to say, it ignores relations which can be deduced only by understanding several sentences within a document. As of today, performance of document-level relation extraction methods fall behind the human performance when it comes to cross-sentence reasoning, therefore this approach needs more effort.

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