There are also efforts in encoding text into low-dimensional semantic spaces and extracting relations from textual embeddings. Furthermore, knowledge graph embeddings can be used for RE.

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SREs reduce human efforts and bring better coverage than pattern rules. 
The following are types of SREs:
- Feature-based methods
- Kernel-based methods
- Graphical methods
- Embedding models

Statistical Relation Extraction Models 

Feature based methods design lexical, syntactic, and semantic features for entity pairs and their corresponding context. These features are input into relation classifiers.Many efforts are required to design features, which is a challenge.

Feature-based methods of RE

Kernel-based methods design kernel functions for support vector machines to measure the similarities between relation representations and textual instances. Many efforts are required to design kernel functions, which is a challenge. 


Kernel-based methods of RE

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

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