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Rasa NLU

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

    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 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

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

    See also

    References

    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.

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