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Name Entity Recognition (NER)

Name Entity Recognition (NER) is a task of our processing pipeline that involves identifying and categorizing named entities in the text data. Named entities refer to specific entities such as names of persons, organizations, locations, dates, quantities, and more. We can understand them as classifications of concepts in a given domain.

If you need help to analyze your data and detect important insights, by automatically identifying and categorizing entities, NER enables systems to organize, categorize, and process large volumes of textual data efficiently.

Entity Extraction​

Entity Extraction is one of the components of our NER. It's objective is to identify and extract named entities from text. Entity Extraction focuses on extracting the entities within the text. By using semantic machine learning techniques, our NER systems can automatically detect and classify entities, providing valuable understanding for various applications. #expecificar que esto se basa en extraer

In Nuclia we have developed a NER module that effectively extracts named entities from your data. It can identify entities like persons, organizations, locations, dates, and other relevant entities based on the context and structure of the input data. Our system is capable of automatically detect and classify various types of entities.

Entity Relation​

In addition to Entity Extraction, our NER system also incorporates Entity Relation capabilities. While Entity Extraction focuses on extract, Entity Relation focuses on understanding the relationships between different named entities within the text. By analyzing the context and co-occurrence patterns, our system can uncover meaningful connections and associations between entities, contributing to a deeper understanding of the text data.

The Entity Relation feature of our system enables the extraction of valuable insights. It contributes to building knowledge graphs by capturing the relationships between entities, representing entities as nodes and relationships as edges, creating a structured representation of information. These graphs facilitate semantic search, knowledge discovery, and advanced data analytics.

An example of this feature would be to recognize key persons involved in specific organizations or identify events and their associated entities.