Relation Extraction 关系抽取综述

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Information Extraction v.s. Relation Extraction

Information Extraction: Information extraction is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources.

Relation extraction (RE) is an important task in IE. It focuses on extracting relations between entities. A complete relation RE system consists of

1. a named entity recognizer to identify named entities from text.
2. an entity linker to link entities to existing knowledge graphs.
3. a relational classifier to determine relations between entities by given context (most difficult and important).

Existing Works of RE

RE methods follows the typical supervised setting, from early pattern-based methods, statistical approaches, to recent neural models.

Pattern-based Methods

The early methods use sentence analysis tools to identify syntactic elements in text, then automatically construct pattern rules from these elements. Later work involves larger corpora, more formats of patterns and more efficient ways of extraction.

Statistical Relation Extraction Models

Statistical Methods requires less human efforts so that statistical relation extraction (SRE) has been extensively studied. Approaches includes:

• feature-based methods which design lexical, syntactic and semantic features for entity pairs and their corresponding context (hard to design features).
• kernel-based methods measures the similarities between relation representations and textual instances (hard to design kernel functions).
• 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 (still limited model capacities)