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.
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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?
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.
Healthcare
Improve clinical documentation, data mining research, and automated registry reporting to help accelerate clinical trials.
Related products and services
Google Cloud offers a full suite of natural language products and solutions.
The Natural Language API provides pretrained models that let developers work with natural language understanding features such as sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis.
As part of the suite of AutoML products, AutoML Natural Language enables you to build and deploy custom machine learning models for natural language with minimal effort and machine learning expertise.
