IBM Watson Natural Language Processing Library for Embed
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Enhance your applications with best-in-class Natural Language AI

Introducing IBM Watson NLP Library for Embed, a containerized library designed to empower IBM partners with greater flexibility to infuse powerful natural language AI into their solutions. It combines the best of open source and IBM® Research® NLP algorithms to deliver superior AI capabilities developers can access and integrate into their apps in the environment of their choice. Offered to partners as embeddable AI, a first of its kind software portfolio that offers best of breed AI from IBM.
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Build with IBM natural language embeddable AI Watson NLP Library for Embed helps develop enterprise-ready solutions through robust AI models, extensive language coverage and scalable container orchestration. The library form provides the flexibility to deploy natural language AI in any environment. Flexible and extensible

IBM Watson NLP Library for Embed offers algorithmic choice and modular design so you can integrate NLP capabilities in your solution with greater flexibility to meet your specific needs.

 Run anywhere

Deploy and run your applications on any hybrid multi cloud in the container environment of your choice: local Docker platform, Kubernetes, or serverless containers.

 
Features - Insights Sentiment analysis

Analyze sentiment on a positive, negative, and neutral scale to determine whether a customer is happy or dissatisfied, pinpoint the reason why, and find specific moments where sentiment changed.

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Detects anger, disgust, fear, joy or sadness that is conveyed in the content or by the context around target phrases specified in the targets parameter.

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Analyze and classify desired text using custom label classifier trained in Watson Studio.

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Features - Information extraction Entities extraction

Detect, extract, prepare, redact personally identifiable information (PII), or classify mentions of entities from raw text data. 

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Extract all mentions of keywords, phrases and expressions a from text input. 

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Extract semantic relationships from text which usually occur between two or more entities.

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Extract concepts of interest that are referenced or alluded to and groups words and phrases into semantically similar groups.

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Use cases

Analyze customer and employee feedback on their experiences and satisfaction with products and services, and workplace environment.
Provide knowledge workers with ansers in real-time from a large corpus of information, augmenting awareness and increasing productivity.
Provides actionable insights for all areas of the financial sector to help save hours of manual work in reviewing materials and gathering evidence.
Provide relevant ads and content recommendations based on site content. Ad targeting and media buying, ad insertion in audio/videos.
Monitor brand reputation and perception on social channels. Assess pulse of the market and gauge effectiveness of ad and public relations campaigns.
Utilize NLP to accelerate classifying, sorting, and processing of extracted semi-structures and unstructured data in document processing tasks.
Pricing
Usage blocks List price

Natural Language Processing for Embed

250,000 API calls per month

USD 125 per month
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Optimized with Intel IBM and Intel have long collaborated on data and AI products. Most recently, IBM Research collaborated with Intel to improve Watson NLP Library for Embed and Watson NLU performance with Intel® oneDNN and Tensorflow. Powered by oneAPI, the integrated solution demonstrated benefits of up to 35% in performance throughput1 for key NLP and NLU tasks. 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
Case studies CrushBank

CrushBank empowers IT help desk employees to quickly find the most relevant information to solve customers’ problems, increasing productivity and customer satisfaction.

 LegalMation

LegalMation uses natural language processing to help legal teams draft high-quality litigation work in minutes, freeing time and resources for higher-tier services.
Resources NLP documentation

The Watson Natural Language Processing (NLP) Library for Embed provides natural language processing functions for syntax analysis, and pre-trained models for a wide variety of text processing tasks, in a fully embeddable library.

 IBM AI and Data Science community

Check out the IBM community, where discussions, libraries, blogs and more are available in the topics surrounding all things data and AI.

Embeddable AI

Explore topics around embeddable AI in the IBM Developer site. Read articles, blogs, take courses and join open projects in a vibrant community of experts.
Take the next step

Partner with IBM to embed NLP in your commercial applications.  Try Watson NLP Library for Embed at no cost.

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Footnotes

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