This area of research and development relies on foundational elements from natural language processing (NLP) systems, which map out linguistic elements and structures. NLU seeks to intuit many of the connotations and implications that are innate in human communication such as the emotion, effort, intent, or goal behind a speaker’s statement.
Why is Natural Language Understanding Important?
Human language is fluid, complex and full of subtleties. Two people may read or listen to the same passage and walk away with completely different interpretations. If humans struggle to develop perfectly aligned understanding due to these congenital linguistic challenges, it stands to reason that machines will struggle as well.
The field of NLU is dedicated to developing strategies and techniques for understanding context in individual records and at scale. In order to categorize or tag texts with humanistic dimensions such as emotion, effort, intent, motive, intensity, and more, NLU systems may leverage both rules based and statistical machine learning approaches. Together, these techniques are applied to resolve tasks such as content analysis, topic modeling, machine translation, and question answering. At scale, NLU systems empower analysts to distill large volumes of text into coherent groups without reading them one by one.
What makes Clarabridge’s NLU different?
Clarabridge makes words matter. Developed on top of over a decade of NLP investments, the Clarabridge NLU engine uses rules based and machine learning techniques to extract, tag and score concepts that are relevant to customer experience analysis such as emotion, effort, intent, profanity and more. Users can customize many of these elements to more precisely reflect their business, use case(s) and industry. When combined with the original text and associated source and customer metadata, analysts and front line teams can uncover what customers mean, not just what they say, empowering truly actionable insights.