Feature-based methods design lexical, syntactic and semantic features for
entity pairs and their corresponding context, and then input these features into relation classifiers.

Feature-based Methods

Due to the wide use of support vector machines (SVM), kernel-based methods have been widely explored, which design kernel functions for SVM to measure the similarities between relation representations and textual instances.

Kernel-based Methods

Graphical methods abstract the dependencies between entities, text and relations in the form of directed acyclic graphs, and then use inference models to identify the correct relations.

Graphical Methods 

There are efforts in encoding text into low-dimensional semantic spaces and extracting relations from textual embeddings.

Embedding Models

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

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Relation Extraction Models

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

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 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.

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