Over the last 25 years, the internet has created an explosion of text and data. Consumers today are doing far more than merely buying and selling goods online. They're also soaking up and generating a great deal of valuable data. Because most of this data is text-based and complex, deriving insights from it has been hard.
That's changing, as digital technologies evolve. Solutions that handle unstructured text can be a real game-changer. For instance, by picking up nuances in language, they can make a big difference when it comes to getting the right Google search result. They can also help us interpret social-media chatter to better understand human behavior.
Natural language processing (NLP), the means by which a computer absorbs, dissects, and analyzes text or speech, turns this data into encoded, structured information, then proposes actions based on the output in everyday language. Natural language understanding (NLU), a subset of NLP, parses unstructured inputs, then structures it in a way that both the machine and humans can understand and act upon.
NLU has captured attention in some industries by answering complex business problems. For example, it can help identify adverse events in drug safety reports, improve patient care by mining medical data, and retrieve information from lengthy contract documents. Yet, for the most part, industry hasn't exploited it for insights or invested in analyzing the rich motherlode of unstructured textual data originating from multiple online sources.