IBM and Their NLP Offering
From cutting edge advances to commercial features
One of the most well-known moments in the history of natural language processing must be the time IBM Watson won a competition of Jeopardy.
IBM did not stop there and they have continued to develop natural language processing and natural language understanding.
According to their website the Natural Language Understanding is now underpinned by the same technology that powers IBM Research’s Project Debater with new enhancements to Watson Discovery expected this year.
It is the first commercial release of key NLP capabilities from IBM Research’s Project Debater .
The new features include several features:
- Advanced sentiment analysis.
- Topic clustering.
- Data summarisation.
Build Apps with Natural Language Processing (NLP) | IBM Watson
With IBM Watson Natural Language Processing (NLP) offerings, surface concepts, categories, sentiment, and emotion, and…
They describe NLP as the following:
“Natural language processing (NLP) is one area of artificial intelligence using computational linguistics that provides parsing and semantic interpretation of text, which allows systems to learn, analyze, and understand human language.”
They are offering Watson Natural Language Understanding (NLU). With this it is possible to: “…surface concepts, categories, sentiment, and emotion, and apply knowledge of unique entities in your industry to your data, no matter where it lives.”
They have several suggestions to how you can use NLP from IBM:
- Optimise your advertising with NLP. Ensure proper placement of advertisements based on page content, viewer patterns, and sentiment analysis of social media and other content.
- Improve voice-of-customer analysis. Spot trends in customer feedback with NLP to identify business opportunities, address concerns, reduce churn, and drive revenue.
- Streamline audience segmentation with NLP. Identify key customer groups for market research and campaign personalisation for different segments.
In addition to this you can extract metadata, help recommend similar content, and mine data in repositories.
Several of the large technology companies have NLP offerings and IBM is one of them.
How do they compare to each other?
That is a question for another day.
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