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

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.

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.

Document-Level Relation Extraction

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

University of Michigan - Ann Arbor

Neural relation extraction discovers semantic relations between entities from unstructured text using deep learning methods.

Neural Relation Extraction

Neural relation extraction: a survey

Neural relation extraction (NRE) models introduce neural networks to automatically extract semantic features from text. Compared with SRE models,
NRE methods can effectively capture textual information and generalize to wider range of data.

Neural Relation Extraction Models

Neural Relation Extraction Methods

Supervised neural relation extraction from text uses sentence-level relation extraction approach which requires labeled relation-specific training data. Many of the studies on this task rely on classifying entity pairs according to the particular relations they are assigned to.

Supervised Relation Extraction

Distant supervision aligns triples in a related knowledge graph with the sentences in input text, in order to automatically generate training data. Distant supervision assumes the responsibility to determine which sentence supports which relation and to what degree it expresses the relation of interest.

 Relation Extraction using Distant Supervision

Few-shot learning is a learning method, in which in contrast to regular deep learning methods the amount of available training data is small. The assumption is that reliable algorithms can be built to achieve competitive performance to the models trained with abundant data.

Relation Extraction using Few-Shot Approach

Challenges in neural relation extraction usually regard available data and existing contextual and structural approaches:
- Overlapping triples
- Noise in distant supervision
-  Few-shot instances

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