The term text analytics describes a set of linguistic, lexical, pattern recognition, extraction, tagging/structuring, visualization, and predictive techniques. The term also describes processes that apply these techniques, whether independently or in conjunction with query and analysis of fielded, numerical data, to solve business problems.

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  • The term text analytics describes a set of linguistic, lexical, pattern recognition, extraction, tagging/structuring, visualization, and predictive techniques. The term also describes processes that apply these techniques, whether independently or in conjunction with query and analysis of fielded, numerical data, to solve business problems. These techniques and processes discover and present knowledge – facts, business rules, and relationships – that is otherwise locked in textual form, impenetrable to automated processing. A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. Current approaches to text analytics use natural language processing techniques that focus on specialized domains. Typical subtasks are: * Named Entity Recognition: recognition of entity names (for people and organizations), place names, temporal expressions, and certain types of numerical expressions. * Coreference: identification chains of noun phrases that refer to the same object. For example, anaphora is a type of coreference. * Relationship Extraction: extraction of named relationships between entities in text (en)
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  • The term text analytics describes a set of linguistic, lexical, pattern recognition, extraction, tagging/structuring, visualization, and predictive techniques. The term also describes processes that apply these techniques, whether independently or in conjunction with query and analysis of fielded, numerical data, to solve business problems. (en)
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  • Text analytics (en)
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