spaCy
spaCy is designed for real work, real products and real insights. The library respects your time, and tries not to waste it. It is easy to install and the API is simple and efficient. spaCy excels in large-scale information extraction tasks. It is written in Cython, which is carefully managed for memory. SpaCy is the library to use if your application requires to process large web dumps. spaCy was released in 2015 and has been a industry standard with a large ecosystem. You can choose from a wide range of plugins and integrate them with your machine-learning stack to create custom components and workflows. You can use these components to recognize named entities, part-of speech tagging, dependency parsing and sentence segmentation. Easy extensible with custom components or attributes Model packaging, deployment, workflow management made easy.
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Primer
Machine learning models can transform your knowledge into text-based workflows that are scaleable and human-level. You can either create your own models, retrain our models for your task, or purchase Primer models off the shelf. Primer Automate is available to anyone in your company. No coding or technical skills are required. You can add a structured layer to your data and create an scalable, self-curating knowledgebase that can quickly scan through billions of documents. Quickly find answers to critical questions, track updates in real-time, and generate easy-to-read reports automatically. To find the most important information, process all your documents, emails and social media. Primer Extract makes it easy to quickly and efficiently explore your data using cutting-edge machine learning techniques. Extract offers more than keyword search. It also provides OCR, translation, and image recognition capabilities.
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Google Cloud Natural Language API
Machine learning can provide insightful text analysis that extracts, analyses, and stores text. AutoML allows you to create high-quality custom machine learning models without writing a single line. Natural Language API allows you to apply natural language understanding (NLU). To identify and label fields in a document, such as emails and chats, use entity analysis. Next, perform sentiment analysis to understand customer opinions and find UX and product insights. Natural Language with speech to text API extracts insights form audio. Vision API provides optical character recognition (OCR), which can be used to scan scanned documents. Translation API can understand sentiments in multiple languages. You can use custom entity extraction to identify domain-specific entities in documents. Many of these entities don't appear within standard language models. This allows you to save time and money by not having to do manual analysis. You can create your own machine learning custom models that can classify, extract and detect sentiment.
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Watson Natural Language Understanding
Watson Natural Language Understanding, a cloud native product, uses deep learning to extract metadata such as entities, keywords and categories, sentiments, emotions, relations, and syntax. Text analysis can be used to uncover the topics in your data. It extracts keywords, concepts and categories. Unstructured data can be analyzed in more than 13 languages. Machine learning models for text mining are available that can be used outside-of-the box to provide high accuracy across your content. Watson Natural Language Understanding can be deployed behind your firewall or on any other cloud. Watson Knowledge Studio allows you to train Watson to understand your business language and extract custom insights. You can keep control of your data and have the assurance that it is safe and secure. IBM will not store or collect your data. Our advanced natural language processing service (NLP), gives developers the tools to extract valuable insights from unstructured information.
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