Pattern extraction methods utilize sentence analysis tools to automatically identify syntactic elements in text and construct pattern rules. To achieve better coverage and accuracy, subsequent research has explored larger corpora, diverse pattern formats, and more efficient extraction techniques, though automatically generated patterns still require expert human verification.

Columbia University

Google

The following approaches have been explored for relation extraction, and these methods have bridged the gap between unstructured text and structured knowledge. 
- Pattern mining
- Statistical approaches 
- Deep learning

Pioneering Explorations of Relation Extraction

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 

Most RE methods work in a simplified setting, and mainly focus on training models with large amounts of human annotations to classify two given entities within one sentence into pre-defined relations. To build an effective and robust RE system that can be deployed in the real world, more complex scenarios must be further investigated.  
- Collecting high-quality human annotation is expensive and time-consuming
- Many long-tail relations cannot provide large amounts of training examples 
- Most facts are expressed by long context consisting of multiple sentences
- Using a predefined set to cover those relations with open-ended growth is not easy

Drawbacks of existing RE methods


NRE models introduce neural networks to automatically extract semantic features from text, and can effectively capture textual information and generalize to a wider range of data. NRE studies mainly focus on designing and using different network architectures to capture relational semantics within text. NRE differs from SREs by using word embeddings and position embeddings as inputs so that semantic meanings of words can be combined with a specification of the relative distances between words and entities. 


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