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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. 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.

State-of-the-art NER systems for English produce near-human performance. For example, the best system entering MUC-7 scored 93.39% of F-measure while human annotators scored 97.60% and 96.95%.[1][2] Calais Viewer. Mafait.org - project Thinknowlogy - Fundamentally designed Artificial Intelligence. LingPipe Home. How Can We Help You? 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: Find the names of people, organizations or locations in newsAutomatically classify Twitter search results into categoriesSuggest correct spellings of queries To get a better idea of the range of possible LingPipe uses, visit our tutorials and sandbox. Architecture LingPipe's architecture is designed to be efficient, scalable, reusable, and robust.

Latest Release: LingPipe 4.1.2 Intermediate Release The latest release of LingPipe is LingPipe 4.1.2, which patches some bugs and documentation. Migration from LingPipe 3 to LingPipe 4 LingPipe 4.1.2 is not backward compatible with LingPipe 3.9.3. Programs that compile in LingPipe 3.9.3 without deprecation warnings should compile and run in Lingpipe 4.1.2. 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. Input: one or more unstructured texts. Output: DyNetML files and CS files. AutoMap is designed to work seamlessly with ORA. AutoMap enables the extraction of information from texts using Network Text Analysis methods.

AutoMap exists as part of a text mining suite that includes a series of pre-processors for cleaning the raw texts so that they can be processed and a set of post-processor that employ semantic inferencing to improve the coding and deduce missing information. AutoMap uses parts of speech tagging and proximity analysis to do computer-assisted Network Text Analysis (NTA). AutoMap subsumes classical Content Analysis by analyzing the existence, frequencies, and covariance of terms and themes. AutoMap has been implemented in Java 1.7. It can operate in both a front end with gui, and backend mode.