Open Source Python Natural Language Processing (NLP) Tools

Python Natural Language Processing (NLP) Tools

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Browse free open source Python Natural Language Processing (NLP) Tools and projects below. Use the toggles on the left to filter open source Python Natural Language Processing (NLP) Tools by OS, license, language, programming language, and project status.

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  • 1

    Arabic Corpus

    Text categorization, arabic language processing, language modeling

    The Arabic Corpus {compiled by Dr. Mourad Abbas ( http://sites.google.com/site/mouradabbas9/corpora ) The corpus Khaleej-2004 contains 5690 documents. It is divided to 4 topics (categories). The corpus Watan-2004 contains 20291 documents organized in 6 topics (categories). Researchers who use these two corpora would mention the two main references: (1) For Watan-2004 corpus ---------------------- M. Abbas, K. Smaili, D. Berkani, (2011) Evaluation of Topic Identification Methods on Arabic Corpora,JOURNAL OF DIGITAL INFORMATION MANAGEMENT,vol. 9, N. 5, pp.185-192. 2) For Khaleej-2004 corpus --------------------------------- M. Abbas, K. Smaili (2005) Comparison of Topic Identification Methods for Arabic Language, RANLP05 : Recent Advances in Natural Language Processing ,pp. 14-17, 21-23 september 2005, Borovets, Bulgary. More useful references to check: ------------------------------------------- https://sites.google.com/site/mouradabbas9/corpora
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    Downloads: 5 This Week
    Last Update:
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  • 2

    MITRE Annotation Toolkit

    A toolkit for managing and manipulating text annotations

    The MITRE Annotation Toolkit (MAT) is a suite of tools which can be used for automated and human tagging of annotations. Annotation is a process, used mostly by researchers in natural language processing, of enhancing documents with information about the various phrase types the documents contain. MAT supports both UI interaction and command-line interaction, and provides various levels of control over the overall annotation process. It can be customized for specific tasks (e.g., named entity identification, de-identification of medical records). The goal of MAT is not to help you configure your training engine (in the default case, the Carafe CRF system) to achieve the best possible performance on your data. MAT is for "everything else": all the tools you end up wishing you had.
    Downloads: 0 This Week
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