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

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 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

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.

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