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IBM Introduces Advanced Capabilities in Watson’s Technologies to Understand the Language of Business
March 12, 2020 News

 

IBM, the leader in artificial intelligence for business[1], is announcing several new IBM Watson technologies designed to help organisations begin identifying, understanding and analysing some of the most challenging aspects of the English language with greater clarity, for greater insights.

The new technologies represent the first commercialisation of key Natural Language Processing (NLP) capabilities to come from IBM Research’s Project Debater, the only AI system capable of debating humans on complex topics. For example, a new advanced sentiment analysis feature is defined to identify and analyse idioms and colloquialisms for the first time. AI systems find it difficult to understand phrases, like ‘hardly helpful,’ or ‘hot under the collar,’ because they are difficult for algorithms to spot. Businesses can begin analysing such language data with the advanced sentiment analysis using Watson APIs for a more holistic understanding of their operations. Further, IBM is bringing technology from IBM Research for understanding business documents, such as PDF’s and contracts, to also add to their AI models.

“Language is a tool for expressing thought and opinion, as much as it is a tool for information,” said Rob Thomas, General Manager, IBM Data and AI. “This is why we’re harvesting technology from Project Debater and integrating it into Watson – to enable businesses to capture, analyze, and understand more from human language and start to transform how they utilize intellectual capital that’s codified in data.”

IBM is announcing that it plans to integrate Project Debater technologies into Watson throughout the year, with a focus on advancing clients’ ability to exploit natural language:

  1. Analysis – Advanced Sentiment Analysis. IBM has enhanced sentiment analysis to be able to better identify and understand complicated word schemes like idioms (phrases and expressions) and so-called, sentiment shifters, which are combinations of words that, together, take on new meaning, such as, “hardly helpful.” This month will see this technology being integrated into Watson Natural Language Understanding. In addition, we are announcing a new classification technology that will enable clients to create AI models that can more easily classify clauses that occur in business documents, like procurement contracts. The new capability, based on Project Debater’s deep learning-based classification technology, can learn from as few as several hundred samples to do new classifications quickly and easily. It is planned to be added to Watson Discovery later this year.
  2. Briefs – Summarisation. This technology pulls textual data from a variety of sources to provide users with a summary of what is being said and written about a particular topic. An early version of Summarisation was leveraged at The GRAMMYS this year to analyse over 18 million articles, blogs and bios to produce bite-sized insights on hundreds of GRAMMY artists and celebrities.  Across www.grammy.com, the data was infused into the red carpet live stream, on-demand videos and photos, to give fans deeper context about the leading topics of the night. It is planned to be added to IBM Watson Natural Language Understanding later in the year.
  3. Clustering – Advanced Topic Clustering. Building on insights gained from Project Debater, new topic clustering techniques will enable users to “cluster” incoming data to create meaningful “topics” of related information, which can then be analysed. The technique is planned to be integrated into Watson Discovery later this year, which will also allow subject matter experts to customise and fine-tune the topics to reflect the language of specific businesses or industries, like insurance, healthcare and manufacturing.

IBM, has long been a leader in NLP, developing technologies that enable computer systems to learn, analyse and understand human language – including sentiment, dialects, intonations, and more – with increasing accuracy and speed. IBM has brought its NLP technology, much of which was born in IBM Research, to market via Watson. Product such as, Watson Discovery for document understanding, IBM Watson Assistant for virtual agents, and Watson Natural Language Understanding for advanced sentiment analysis, are all infused with NLP.

ESPN Fantasy Football uses Watson Discovery and Watson Knowledge Studio to analyse millions of football data sources each day during the season to offer millions of fantasy football players real-time insights. By processing natural language, Watson identifies the tone and sentiment of news articles, blogs, forums, rankings, projections, podcasts and tweets that cover everything from locker room insights to injury analysis. ESPN Fantasy Football surfaces these insights in player cards that snapshot the “boom” and “bust” potential of each player, as well as a “Player Buzz” section that summarises the positive or negative commentary about a player.

KPMG, a multinational professional services network, and one of the Big Four accounting organizations, worked with IBM to create an AI solution based on a variety of Watson services, including Watson Natural Language Understanding. This technology makes it more effective for companies to identify, claim and retain potential R&D income tax credits. Developed by KPMG, the solution can help clients increase the amount of R&D income tax credits they capture because the Watson technology is able to review more documentation quickly while minimizing disruption to the client’s business.

In the past year, KPMG clients have seen more potential for R&D tax credits, with some projects even seeing more than a 1000% increase in the number of documents reviewed. The solution helps clients uncover more potential activities that qualify for additional income tax credits, while reducing business disruption. As a result, engineers and scientists can stay focused on innovative R&D work by spending less time on income tax compliance activities.

“The thumb rule for the success of any NLP project is simple and straight forward – the better the ability to understand language and complex topics, better the ability to deliver outcomes for the business. A recent study by Greyhound Research confirms the increasing use of chatbots and similar intelligent platforms using NLP by organizations across the globe. The study confirms that while over 73 % large organizations globally are either already running a project or looking to launch one in the next 12 months. The fact that the work on NLP is moving from IBM’s research labs to commercial availability now, goes to show the progress that has been made with Project Debater and the technology. Also, the project’s effort to cluster a variety of incoming data by topics, summarise it for business users and provide enhanced sentiment analysis is a significant step forward in the field of NLP. Such technologies can significantly benefit organizations looking for better insights from their data and, most importantly, establish a better connection with customers and stakeholders.” – Sanchit Vir Gogia, Chief Analyst, Founder and CEO, Greyhound Research.

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