Compare the Top Natural Language Processing Software that integrates with AWS App Mesh as of October 2024

This a list of Natural Language Processing software that integrates with AWS App Mesh. Use the filters on the left to add additional filters for products that have integrations with AWS App Mesh. View the products that work with AWS App Mesh in the table below.

What is Natural Language Processing Software for AWS App Mesh?

Natural language processing (NLP) software analyzes both written and spoken human languages and interprets them for translation, deep learning and automation purposes. Natural language processing software may also include natural language understanding (NLU) capabilities. Compare and read user reviews of the best Natural Language Processing software for AWS App Mesh currently available using the table below. This list is updated regularly.

  • 1
    Amazon Textract
    Amazon Textract is a fully managed machine learning service that automatically extracts text and data from scanned documents that goes beyond simple optical character recognition (OCR) to identify, understand, and extract data from forms and tables. Many companies today extract data from scanned documents, such as PDF's, tables and forms, through manual data entry (that is slow, expensive and prone to errors), or through simple OCR software that requires manual configuration which needs to be updated each time the form changes to be usable. To overcome these manual processes, Textract uses machine learning to instantly read and process any type of document, accurately extracting text, forms, tables, and, other data without the need for any manual effort or custom code. With Textract you can quickly automate manual document activities, enabling you to process millions of document pages in hours.
  • 2
    Amazon Lex
    Amazon Lex is a service for building conversational interfaces into any application using voice and text. Amazon Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions. With Amazon Lex, the same deep learning technologies that power Amazon Alexa are now available to any developer, enabling you to quickly and easily build sophisticated, natural language, conversational bots (“chatbots”). With Amazon Lex, you can build bots to increase contact center productivity, automate simple tasks, and drive operational efficiencies across the enterprise. As a fully managed service, Amazon Lex scales automatically, so you don’t need to worry about managing infrastructure.
  • 3
    Amazon Comprehend
    Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. No machine learning experience required. There is a treasure trove of potential sitting in your unstructured data. Customer emails, support tickets, product reviews, social media, even advertising copy represents insights into customer sentiment that can be put to work for your business. The question is how to get at it? As it turns out, Machine learning is particularly good at accurately identifying specific items of interest inside vast swathes of text (such as finding company names in analyst reports), and can learn the sentiment hidden inside language (identifying negative reviews, or positive customer interactions with customer service agents), at almost limitless scale. Amazon Comprehend uses machine learning to help you uncover the insights and relationships in your unstructured data.
  • 4
    Amazon Comprehend Medical
    Amazon Comprehend Medical is a HIPAA-eligible natural language processing (NLP) service that uses machine learning to extract health data from medical text–no machine learning experience is required. Much of health data today is in free-form medical text like doctors’ notes, clinical trial reports, and patient health records. Manually extracting the data is a time consuming process, while automated rule-based attempts to extract the data don’t capture the full story as they fail to take context into account. As a result, the data remains unusable in large-scale analytics needed to advance the healthcare and life sciences industry and improve patient outcomes and create efficiencies.
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