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Natural Language Processing

How Is External Data Used in Natural Language Processing?

Natural language processing (NLP) fuses mathematics and computer science with artificial intelligence. It takes in spoken, written, and image-based language and applies statistical rules to process the language and turn it into commands or to enable computers to communicate with people.

Unfortunately, these machine learning models face difficulties in this use case theme: mainly, they cannot read tone. However, they use contextual clues in the surrounding text to compensate.

How are Machine Learning Models Used in Natural Language Processing?

In the earliest days of natural language processing, developers applied manually-generated rules-based algorithms to language. However, the field advanced to the use of statistical models—then to neural models. In contrast with older rules-based models, these programs can take in huge amounts of data and generate outputs (conversations, translations) based on the most likely results without having to tag individual parts of speech.

Which Companies Lead the Way in Building Advanced Analytics and Machine Learning Products in This Field?

Master of Code
Founded in 2005, Master of Code develops NLP products, especially chatbots, compatible with Facebook, WhatsApp, Google, and more. They also count Amazon, Azure, and IBM among their partners.
A leader in conversational AI applications in the IoT field, SoundHound enables people to discover and identify songs from snippets of music. They also enable people to give orders to their household appliances.
Grammarly offers free text-based NLP programs that help people improve their spelling and grammar. It even offers analysis and suggestions for improving perceived tone in written speech.

Natural Language Processing Use Cases

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