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.
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.
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.
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.
