Amazon Comprehend vs Plasticity vs SpaCy

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Amazon Comprehend

50
138
+ 1
0
Plasticity

3
10
+ 1
0
SpaCy

217
291
+ 1
14
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Pros of Amazon Comprehend
Pros of Plasticity
Pros of SpaCy
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      • 12
        Speed
      • 2
        No vendor lock-in

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      Cons of Amazon Comprehend
      Cons of Plasticity
      Cons of SpaCy
      • 2
        Multi-lingual
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        • 1
          Requires creating a training set and managing training

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        - No public GitHub repository available -
        - No public GitHub repository available -

        What is 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.

        What is Plasticity?

        Today's personal assistants and conversational interfaces fail to handle variations in a user's wording or multiple requests in one sentence. We take a language-based semantic approach to handle complex dialogue.

        What is 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.

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        Jobs that mention Amazon Comprehend, Plasticity, and SpaCy as a desired skillset
        What companies use Amazon Comprehend?
        What companies use Plasticity?
        What companies use SpaCy?

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        What tools integrate with Amazon Comprehend?
        What tools integrate with Plasticity?
        What tools integrate with SpaCy?
          No integrations found
          What are some alternatives to Amazon Comprehend, Plasticity, and SpaCy?
          IBM Watson
          It combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a "question answering" machine.
          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.
          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