Relation extraction is the process of identifying and extracting relationships between entities from text or other data sources. This can be accomplished using natural language processing (NLP) techniques such as named entity recognition (NER) and dependency parsing.
Relation extraction is often used in a variety of applications, including information extraction, knowledge base construction, and text summarization. In information extraction, relation extraction can be used to identify and extract specific types of relationships between entities, such as who is married to whom, or which company a person works for. This information can then be used to populate a database or knowledge base with structured data.
In knowledge base construction, relation extraction can be used to extract relationships between entities from unstructured text and add them to a structured knowledge base. This can help to expand the scope and coverage of the knowledge base, and make it more useful for a variety of applications.
Text summarization is another application in which relation extraction can be used. By identifying and extracting relationships between entities in a text, it is possible to generate a summary of the main points or themes of the text. This can be useful for tasks such as news summarization or summarizing large volumes of text for easier consumption.
Overall, relation extraction is a powerful tool for extracting structured data from unstructured text.
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[17x Jan 2017]