Insights from customers
Use entity analysis to find and label fields within a document—including emails, chat, and social media—and then sentiment analysis to understand customer opinions to find actionable product and UX insights.
Multimedia and multilingual support
Natural Language with Speech-to-Text API extracts insights from audio. Vision API adds optical character recognition (OCR) for scanned docs. Translation API understands sentiments in multiple languages.
Extract key document entities that matter
Use custom entity extraction to identify domain-specific entities within documents—many of which don’t appear in standard language models—without having to spend time or money on manual analysis.
Natural Language API demo
Try the API
Try the API
Train your own high-quality machine learning custom models to classify, extract, and detect sentiment with minimum effort and machine learning expertise using Vertex AI for natural language, powered by AutoML. You can use the AutoML UI to upload your training data and test your custom model without a single line of code.
Natural Language API
The powerful pre-trained models of the Natural Language API empowers developers to easily apply natural language understanding (NLU) to their applications with features including sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis.
Healthcare Natural Language AI
Gain real-time analysis of insights stored in unstructured medical text. Healthcare Natural Language API allows you to distill machine-readable medical insights from medical documents, while AutoML Entity Extraction for Healthcare makes it simple to build custom knowledge extraction models for healthcare and life sciences apps—no coding skills required.
“By using custom entity extraction within AutoML Natural Language, we can use large data sets to train our model and continually improve the process, no matter where the document comes from.”
Cloud Natural Language API
Provide natural language understanding technologies, such as sentiment analysis, entity recognition, and other text annotations to developers.
Build custom ML models with AutoML for natural language data
Create models to classify documents, identify entities in documents, or analyze the prevailing emotional attitude in a document.