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NeuronBlocks
NeuronBlocks

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prose
prose

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+ 1
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NeuronBlocks vs prose: What are the differences?

NeuronBlocks: A deep learning modeling toolkit by Microsoft. It is a NLP deep learning modeling toolkit that helps engineers/researchers to build end-to-end pipelines for neural network model training for NLP tasks. The main goal of this toolkit is to minimize developing cost for NLP deep neural network model building, including both training and inference stages; prose: 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.

NeuronBlocks and prose can be primarily classified as "NLP / Sentiment Analysis" tools.

NeuronBlocks and prose are both open source tools. It seems that prose with 2.03K GitHub stars and 95 forks on GitHub has more adoption than NeuronBlocks with 1.08K GitHub stars and 153 GitHub forks.

What is NeuronBlocks?

It is a NLP deep learning modeling toolkit that helps engineers/researchers to build end-to-end pipelines for neural network model training for NLP tasks. The main goal of this toolkit is to minimize developing cost for NLP deep neural network model building, including both training and inference stages.

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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                What are some alternatives to NeuronBlocks 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.
                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.
                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.
                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.
                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.
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