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Saturday, April 13, 2019

Natural Language and Intent with Ambiguity

Good simple explanation from Tableau on Intent.   Have used the concept now in several projects, and of course there is ambiguity, beyond dictionary-definition,  in the use of many terms within a company.   The ambiguity in context is important to consider.

Machine learning, natural language meet to understand intent
 By Mark Jewett, VP of Marketing, Tableau

Machine learning and natural language processing promise to better translate human curiosity into pertinent answers. If true, these smart capabilities will broaden the use of analytics and reach people who are less comfortable dealing with data. It will all start with helping machines learn to interpret human intent. The key is semantics.

Sometimes intent is simple and explicit, like asking Siri or Alexa if a flight is delayed. This question has clear intention and a simple response—returning the flight status answers the question. Such simplicity is seldom the case when it comes to data analysis. Questions are usually more nuanced, making it hard to correctly assume what the user is really looking for. Natural language is even more tricky where ambiguous terms are common.

It’s also difficult for a machine to understand our intent within a limited context. The machine has the data itself but doesn’t grasp the bigger picture in the same way a person with domain expertise can. Asking “How are my sales doing in the Northeast?” is a lot more ambiguous than the flight status example above.

Ambiguity isn’t a new challenge in data analysis. Different groups within an organization may have different definitions or calculations for the same words: for example, the term “profitability”. Some organizations use central dictionaries (also called data catalogs) to reduce ambiguity and create consistency across the organization. These tools can help provide users with the context they need to understand more deeply. .... " 

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