IBM Watson Natural Language Understanding

The natural language AI service for advanced text analytics
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Get more out of your text data

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.

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Text analytics

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.

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Entities

Detect people, places, events, and other types of entities mentioned in your content using our out-of-the-box capabilities.

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Categories (Beta)

Categorize your data with granularity using a five-level classification hierarchy.

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Classifications

Classify text with custom labels to automate workflows, extract insights, and improve search and discovery.

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Concepts

Identify high-level concepts that aren’t necessarily directly referenced in your content.

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Emotions

Extract emotions (joy, anger, sadness, fear, and other feelings) conveyed by specific target phrases or by the document as a whole.

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Sentiment (Beta)

Analyze the sentiment (positive, negative, or neutral) towards specific target phrases and of the document as a whole.

Learn more about sentiment (Beta)
Semantic roles

Parse sentences into subject-action-object form and identify entities and keywords that are subjects or objects of an action.

Learn more about semantic roles

Case study

amv-coworkcenter1439. Confident young entrepreneur using laptop in creative office. Male business professional is wearing headphones while working. He is sitting at desk at coworking space. Green bookcases with books and plants. Modern decor.
Mushi Lab

Mushi Lab created Clearscope to analyze high-performing content and identify actionable recommendations, resulting in 15% month-over-month revenue growth.

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How NLU pricing works

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.

Lite

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.

Standard

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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Optimized with Intel IBM Watson Natural Language Understanding powered by Intel processors

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.

Read the solution brief
Intel oneAPI tools accelerating IBM Watson Natural Language Processing Library

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.

Read the IBM Research blog
IBM Watson NLU and accelerators on Intel Xeon-based infrastructure

A quick overview of the integration of IBM Watson NLU and accelerators on Intel Xeon-based infrastructure with links to various resources.

Read the overview

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Take the next step

Get started now with IBM Watson Natural Language Understanding and test drive the natural language AI service on IBM Cloud.

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Footnotes

1 "Improving Watson NLP performance in IBM products through Intel Optimizations," (IBM Research technical note: October 2022).