Named entity recognition

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Named-entity recognition. Named-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify elements in text into pre-defined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.

Named-entity recognition

Most research on NER systems has been structured as taking an unannotated block of text, such as this one: Jim bought 300 shares of Acme Corp. in 2006. And producing an annotated block of text that highlights the names of entities: [Jim]Person bought 300 shares of [Acme Corp.]Organization in [2006]Time. In this example, a person name consisting of one token, a two-token company name and a temporal expression have been detected and classified. Calais Viewer. - project Thinknowlogy - Fundamentally designed Artificial Intelligence. LingPipe Home. How Can We Help You?

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Get the latest version: Free and Paid Licenses/DownloadsLearn how to use LingPipe: Tutorials Get expert help using LingPipe: Services Join us on Facebook What is LingPipe? LingPipe is tool kit for processing text using computational linguistics. LingPipe is used to do tasks like: Text mining. AutoMap: Project. Overview | People | Sponsors | Publications | Hardware Requirements | Software | Training & Sample Data AutoMap is a text mining tool developed by CASOS at Carnegie Mellon.

AutoMap: Project

Input: one or more unstructured texts. Output: DyNetML files and CS files.