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MonkeyLearn vs prose

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MonkeyLearn
15
44
+ 1
1
prose
4
7
+ 1
0
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MonkeyLearn vs prose: What are the differences?

Developers describe MonkeyLearn as "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. On the other hand, prose is detailed as "A Golang library for text processing". prose is a natural language processing library (English only, at the moment) in pure Go. It supports tokenization, segmentation, part-of-speech tagging, and named-entity extraction.

MonkeyLearn and prose can be categorized as "NLP / Sentiment Analysis" tools.

prose is an open source tool with 2.02K GitHub stars and 95 GitHub forks. Here's a link to prose's open source repository on GitHub.

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Pros of MonkeyLearn
Pros of prose
  • 1
    Easy to use
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    - No public GitHub repository available -

    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 prose?

    prose is a natural language processing library (English only, at the moment) in pure Go. It supports tokenization, segmentation, part-of-speech tagging, and named-entity extraction.

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    Jobs that mention MonkeyLearn and prose as a desired skillset
    Engineering Manager, API Client
    Postman
    San Francisco, United States
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    ageSimpleRequests+12
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      What are some alternatives to MonkeyLearn and prose?
      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.
      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.
      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.
      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.
      See all alternatives

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