IBM Watson® Natural Language Understanding uses deep learning to extract meaning and metadata from unstructured text data. Get underneath your data using text analytics to extract categories, classification, entities, keywords, sentiment, emotion, relations and syntax.
Natural Language Understanding is a best-of-breed text analytics service that can be integrated into an existing data pipeline that supports 13 languages depending on the feature. NLU is hosted in Dallas, Washington, D.C., Frankfurt, and Sydney.
- Deploy Watson Natural Language Understanding behind your firewall or on any cloud.
- Train Watson to understand the language of your business and extract customized insights with Watson Knowledge Studio.
- Surface real-time actionable insights to provide your employees with the tools they need to pull meta-data and patterns from massive troves of data.
Similar NLU capabilities are part of the IBM Watson NLP Library for Embed®, a containerized library for IBM partners to integrate in their commercial applications.
USD 6.13 million in benefits over three years¹
383% ROI over three years²
50% reduction in time spent on information-gathering tasks³
5% annual increase in revenue³
Train Watson to understand the language of your business and extract customized insights with Watson Knowledge Studio.
Surface real-time actionable insights to provides your employees with the tools they need to pull meta-data and patterns from massive troves of data.
Deploy Watson Natural Language Understanding behind your firewall or on any cloud.
Detect people, places, events, and other types of entities mentioned in your content using our out-of-the-box capabilities.
Categorize your data with granularity using a five-level classification hierarchy.
Classify text with custom labels to automate workflows, extract insights, and improve search and discovery.
Identify high-level concepts that aren’t necessarily directly referenced in your content.
Extract emotions (joy, anger, sadness, fear, and other feelings) conveyed by specific target phrases or by the document as a whole.
Analyze the sentiment (positive, negative, or neutral) towards specific target phrases and of the document as a whole.
Understand the relationship between two entities within your content and identify the type of relation.
Quickly extract information from a document such as author, title, images, and publication dates.
Parse sentences into subject-action-object form and identify entities and keywords that are subjects or objects of an action.
Case studies
IBM Watson Natural Language Understanding is an offering within the IBM Cloud.
For detailed Natural Language Understanding pricing, view the following table. For additional information about our broader pricing models and approaches, visit our IBM Cloud pricing overview.
Please visit our pricing calculator here, which gives an estimate of your costs based on the number of custom models and NLU items per month.
The Lite plan is perpetual for 30,000 NLU items and one custom model per calendar month. Once you reach the 30,000 NLU items limit in a calendar month, your NLU instance will be suspended and reactivated on the first day of next calendar month. We recommend the Lite Plan for POC's and the standard plan for higher usage production purposes.
The Standard Plan supports more than 5M items/month starting at USD 0.003 per item. The standard version includes unlimited custom entities and relations models trained with WKS for USD 800 and custom classification models for USD 25.
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IBM Watson NLP Library for Embed, powered by Intel processors and optimized with Intel software tools, uses deep learning techniques to extract meaning and meta data from unstructured data.
Integrating Intel’s OneAPI and IBM Watson’s NLP Library can accelerate the performance of various NLP tasks, including sentiment analysis, topic modeling, named entity recognition, keyword extraction, text classification, entity categorization, and word embeddings.
A quick overview of the integration of IBM Watson NLU and accelerators on Intel Xeon-based infrastructure with links to various resources.
Footnotes
¹“The Total Economic Impact™ Of IBM Watson Natural Language Processing (NLP) Solutions,” (Forrester: 2021), 3.
²“The Total Economic Impact™ Of IBM Watson Natural Language Processing (NLP) Solutions,” (Forrester: 2021), 1.
³“The Total Economic Impact™ Of IBM Watson Natural Language Processing (NLP) Solutions,” (Forrester: 2021), 2.
4"Improving Watson NLP performance in IBM products through Intel Optimizations," (IBM Research technical note: October 2022)