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What is Natural Language Processing?

Natural language processing (NLP) uses machine learning to reveal the structure and meaning of text. With natural language processing applications, organizations can analyze text and extract information about people, places, and events to better understand social media sentiment and customer conversations.

Learn how to derive insights from unstructured natural language text using Google machine learning.

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Natural language processing defined

As a branch of artificial intelligence, NLP (natural language processing), uses machine learning to process and interpret text and data. Natural language recognition and natural language generation are types of NLP.

A subtopic of NLP, natural language understanding (NLU) is used to comprehend what a body of text really means. NLU can categorize, archive, and analyze text. NLP goes a step further to enable decision-making based on that meaning.

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What is natural language processing used for?

Natural language processing applications are used to derive insights from unstructured text-based data and give you access to extracted information to generate new understanding of that data. Natural language processing examples can be built using Python, TensorFlow, and PyTorch.

Customer sentiment

Use entity analysis to find and label fields within documents and channels to better understand customer opinions and find product and UX insights.

Receipt and invoice understanding

Extract entities to identify common entries in receipts and invoices, like dates or prices, to understand relationships between request and payment.

Document analysis

Use custom entity extraction to identify domain-specific entities within documents without having to spend time or money on manual analysis.

Content classification

Classify documents by common entities, domain-specific customized entities, or 700+ general categories, like sports and entertainment.

Trend spotting

Aggregate news with text that lets marketers extract relevant content about their brands from online news, articles, and other data sources.


Improve clinical documentation, data mining research, and automated registry reporting to help accelerate clinical trials.