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Rasa NLU

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Rasa NLU
Rasa nlu horizontal purple.svg
Developer(s)Rasa Technologies, various
Initial releaseDecember 2016.[1]
Repositorygithub.com/RasaHQ/rasa_nlu
Written inPython
Engine
    TypeNatural language processing
    LicenseApache License
    WebsiteRasa Forum

    Amazon.com Logo.png Search Rasa NLU on Amazon.

    Rasa NLU is an open-source library for Natural Language Processing[2][3]. The library is published under the Apache 2.0 license and enables intent classification and entity extraction of natural language using word embeddings for the use in AI assistants and chatbots.[4]

    Unlike most NLU solutions it is hosted completely on-premise making it a viable option for companies handling sensitive data or developing in house expertise.[5] Rasa NLU integrates with common backend-systems providing pre-trained word vectors like SpaCy or fastText. It is also possible to use tensorflow components to train new custom word vectors on a specific dataset.

    Recently it was announced as one of the top 10 open source machine learning projects on Github.[6]

    Main Features[edit]

    • Intent classification[7]
    • Multi-intent classification[8].
    • Named entity recognition[9].
    • Pre-trained word vectors
    • Custom supervised word embeddings[10]

    See also[edit]

    • Natural language processing
    • Natural language understanding
    • Chatbot

    References[edit]

    1. Mannes, John. "Rasa NLU gives developers an open source solution for natural langauge processing". Techcrunch.
    2. "Open-source intent recognition in NLP & NLU". Nology. Retrieved 31 August 2018.
    3. Rodriguez, Jesus. "Technology Fridays: An overview of Rasa, the best NLP Platform you never heard of". Retrieved 31 August 2018.
    4. "Rasa NLU". Rasa. Retrieved 31 August 2018.
    5. Olson, Parmy. "Google, Microsoft And Startups Are Going To War On Chatbot Technology". Forbes. Forbes. Retrieved 18 February 2019.
    6. WIGGERS, KYLE. "Top ML Projects on Github". VentureBeat. Retrieved 18 February 2019.
    7. "Understanding the NLU pipleine". Rasa. Retrieved 31 August 2018.
    8. Petraityte, Justina. "How to handle multiple intents per input using Rasa NLU TensorFlow pipeline". Rasa. Retrieved 31 August 2018.
    9. "Entity Extraction". Rasa. Retrieved 31 August 2018.
    10. Nichol, Alan. "Supervised Word Vectors from Scratch". Medium.com. Rasa. Retrieved 18 February 2019.

    External links[edit]

    deleted unrelated information about company and other products, added independent sources, redefined main features, made a stub for now[edit]

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