Alternatives to Amazon Comprehend logo

Alternatives to Amazon Comprehend

IBM Watson, SpaCy, rasa NLU, Transformers, and Gensim are the most popular alternatives and competitors to Amazon Comprehend.
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What is Amazon Comprehend and what are its top alternatives?

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.
Amazon Comprehend is a tool in the NLP / Sentiment Analysis category of a tech stack.

Top Alternatives to Amazon Comprehend

  • IBM Watson
    IBM Watson

    It combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a "question answering" machine. ...

  • SpaCy
    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. ...

  • rasa NLU
    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. ...

  • Transformers
    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. ...

  • Gensim
    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. ...

  • Google Cloud Natural Language API
    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. ...

  • FastText
    FastText

    It is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. ...

  • Spark NLP
    Spark NLP

    It is a Natural Language Processing library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines that scale easily in a distributed environment. It comes with 160+ pretrained pipelines and models in more than 20+ languages. ...

Amazon Comprehend alternatives & related posts

IBM Watson logo

IBM Watson

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A question-answering computer system capable of answering questions posed in natural language
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PROS OF IBM WATSON
  • 3
    Api
  • 1
    Prebuilt front-end GUI
  • 1
    Intent auto-generation
  • 1
    Custom webhooks
  • 1
    Disambiguation
CONS OF IBM WATSON
  • 1
    Multi-lingual

related IBM Watson posts

SpaCy logo

SpaCy

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Industrial-Strength Natural Language Processing in Python
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PROS OF SPACY
  • 11
    Speed
  • 2
    No vendor lock-in
CONS OF SPACY
  • 1
    Requires creating a training set and managing training

related SpaCy posts

rasa NLU logo

rasa NLU

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Conversational AI platform, for personalized conversations at scale
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PROS OF RASA NLU
  • 8
    Open Source
  • 6
    Docker Image
  • 6
    Self Hosted
  • 3
    Comes with rasa_core
  • 1
    Enterprise Ready
CONS OF RASA NLU
  • 4
    No interface provided
  • 3
    Wdfsdf

related rasa NLU posts

Transformers logo

Transformers

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State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0
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PROS OF TRANSFORMERS
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    CONS OF TRANSFORMERS
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      related Transformers posts

      Gensim logo

      Gensim

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      A python library for Topic Modelling
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      PROS OF GENSIM
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        CONS OF GENSIM
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          related Gensim posts

          Biswajit Pathak
          Project Manager at Sony · | 6 upvotes · 97.5K views

          Can you please advise which one to choose FastText Or Gensim, in terms of:

          1. Operability with ML Ops tools such as MLflow, Kubeflow, etc.
          2. Performance
          3. Customization of Intermediate steps
          4. FastText and Gensim both have the same underlying libraries
          5. Use cases each one tries to solve
          6. Unsupervised Vs Supervised dimensions
          7. Ease of Use.

          Please mention any other points that I may have missed here.

          See more
          Google Cloud Natural Language API logo

          Google Cloud Natural Language API

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          Derive insights from unstructured text using Google machine learning
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          PROS OF GOOGLE CLOUD NATURAL LANGUAGE API
            Be the first to leave a pro
            CONS OF GOOGLE CLOUD NATURAL LANGUAGE API
            • 2
              Multi-lingual

            related Google Cloud Natural Language API posts

            FastText logo

            FastText

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            Library for efficient text classification and representation learning
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            PROS OF FASTTEXT
            • 1
              Simple
            CONS OF FASTTEXT
            • 1
              No step by step API support
            • 1
              No in-built performance plotting facility or to get it
            • 1
              No step by step API access

            related FastText posts

            Biswajit Pathak
            Project Manager at Sony · | 6 upvotes · 97.5K views

            Can you please advise which one to choose FastText Or Gensim, in terms of:

            1. Operability with ML Ops tools such as MLflow, Kubeflow, etc.
            2. Performance
            3. Customization of Intermediate steps
            4. FastText and Gensim both have the same underlying libraries
            5. Use cases each one tries to solve
            6. Unsupervised Vs Supervised dimensions
            7. Ease of Use.

            Please mention any other points that I may have missed here.

            See more
            Spark NLP logo

            Spark NLP

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            State of the Art Natural Language Processing
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            PROS OF SPARK NLP
              Be the first to leave a pro
              CONS OF SPARK NLP
                Be the first to leave a con

                related Spark NLP posts