Natural language understanding (NLU) is a subfield of natural language processing (NLP) that enables machine reading comprehension. While both understand human language, NLU goes beyond the structural understanding of language to interpret intent, resolve context and word ambiguity, and even generate human language on its own. NLU is designed for communicating with non-programmers – to understand their intent and act on it. NLU algorithms tackle the extremely complex problem of semantic interpretation – that is, understanding the intended meaning of spoken or written language, with all the subtleties, of human error, such as mispronunciations or fragmented sentences.
How does it work?
After your data has been analyzed by NLP to identify parts of speech, etc., NLU utilizes context to discern meaning of fragmented and run-on sentences to execute intent. For example, imagine a voice command to Siri or Alexa:
Siri / Alexa play me a …. um song by ... um …. oh I don’t know …. that band I like …. the one you played yesterday …. The Beach Boys … no the bass player … Dick something …
What are the chances of Siri / Alexa playing a song by Dick Dale? That’s where NLU comes in.
NLU reduces the human speech (or text) into a structured ontology – a data model comprising of a formal explicit definition of the semantics (meaning) and pragmatics (purpose or goal). The algorithms pull out such things as intent, timing, location and sentiment.
The above example might break down into:
Play song [intent] / yesterday [timing] / Beach Boys [artist] / bass player [artist] / Dick [artist]
By piecing together this information you might just get the song you want!
NLU has many important implications for businesses and consumers alike. Here are some common applications:
• Conversational interfaces – BOTs that can enhance the customer experience and deliver efficiency.
• Virtual assistants – natural language powered, allowing for easy engagement using natural dialogue.
• Call steering – allowing customers to explain, in their own words, why they are calling rather than going through predefined menus.
• Smart listener – allowing users to optimize speech output applications.
• Information summarization – algorithms that can ‘read’ long documents and summarize the meaning and/or sentiment.
• Pre-processing for machine learning (ML) – the information extracted can then be fed into a machine learning recommendation engine or predictive model. For example, NLU and ML are used to sift through novels to predict which would make hit movies at the box office!
Imagine the power of an algorithm that can understand the meaning and nuance of human language in many contexts, from medicine to law to the classroom. As the volumes of unstructured information continue to grow exponentially, we will benefit from computers’ tireless ability to help us make sense of it all.
Natural Language Processing: What it is and why it matters
White paper: Text Analytics for Executives: What Can Text Analytics Do for Your Organization?
SAS® Text Analytics for Business Applications: Concept Rules for Information Extraction Models, by Teresa Jade, Biljana Belamaric Wilsey, and Michael Wallis
Unstructured Data Analysis: Entity Resolution and Regular Expressions in SAS®, by Matthew Windham