The pioneering methods use sentence analysis tools to identify syntactic elements in text, then automatically construct pattern rules from these elements. In order to extract patterns with better coverage and accuracy, later work involves larger corpora, more formats of patterns, and more efficient ways of extraction.

Pattern Extraction Models

Statistical methods bring better coverage and require less human
efforts. Thus statistical relation extraction (SRE) has been extensively studied.

Statistical Relation Extraction Models

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

Hamilton College

Han, X. (n.d.). More Data, More Relations, More Context and More Openness:
A Review and Outlook for Relation Extraction *, Retrieved June 4, 2022, from https://arxiv.org/pdf/2004.03186.pdf

More Relations, More Context and More Openness:
A Review and Outlook for Relation Extraction

(1) collecting high-quality human annotations is expensive and time-consuming


(2) many long-tail relations cannot provide large amounts
of training examples


(3) most facts are expressed by long context consisting of multiple sentences

(4) using a pre-defined set to cover those relations with open-ended growth is difficult

Limitations of Existing RE Methods

Feasible Approaches Leading to Better RE
Abilities

Relation Extraction Models

 Key challenges:
(1) learning from text or names
(2) datasets towards special interests

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