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MonkeyLearn

13
39
+ 1
1
rasa NLU

99
220
+ 1
23
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MonkeyLearn vs rasa NLU: What are the differences?

What is MonkeyLearn? Text Analysis with Machine Learning. Turn emails, tweets, surveys or any text into actionable data. Automate business workflows and saveExtract and classify information from text. Integrate with your App within minutes. Get started for free.

What is rasa NLU? Open source, drop-in replacement for NLP tools like wit.ai. rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.

MonkeyLearn and rasa NLU can be primarily classified as "NLP / Sentiment Analysis" tools.

rasa NLU is an open source tool with 5.6K GitHub stars and 1.66K GitHub forks. Here's a link to rasa NLU's open source repository on GitHub.

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Pros of MonkeyLearn
Pros of rasa NLU
  • 1
    Easy to use
  • 8
    Open Source
  • 6
    Self Hosted
  • 5
    Docker Image
  • 3
    Comes with rasa_core
  • 1
    Enterprise Ready

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Cons of MonkeyLearn
Cons of rasa NLU
    Be the first to leave a con
    • 3
      No interface provided
    • 1
      Wdfsdf

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    What is MonkeyLearn?

    Turn emails, tweets, surveys or any text into actionable data. Automate business workflows and saveExtract and classify information from text. Integrate with your App within minutes. Get started for free.

    What is rasa NLU?

    rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.

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    What companies use MonkeyLearn?
    What companies use rasa NLU?
    See which teams inside your own company are using MonkeyLearn or rasa NLU.
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    What tools integrate with MonkeyLearn?
    What tools integrate with rasa NLU?

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    What are some alternatives to MonkeyLearn and rasa NLU?
    SpaCy
    It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.
    Gensim
    It is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
    Amazon Comprehend
    Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to discover insights from text. Amazon Comprehend provides Keyphrase Extraction, Sentiment Analysis, Entity Recognition, Topic Modeling, and Language Detection APIs so you can easily integrate natural language processing into your applications.
    Transformers
    It provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
    Google Cloud Natural Language API
    You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage on Google Cloud Storage.
    See all alternatives