Natural Language Processing Strives to Meet Star Trek Standard

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Pollock expects the technology to advance beyond just the opening prompt as well. "Once the customer initially identifies where he wants to go, companies might use natural language for the next level of support too," he says.

Beyond that, there is a wide variety of research happening around natural language in the labs at companies such as Nuance, IBM, Apple, Microsoft, and Google, as well as in the academic community.

Natural language, Miller points out, "will only get better."

"By design," he says, "natural language understanding is closely mated to machine learning resources, big data storage, and deep analytics."

That is precisely the focus of much of the research that Nuance is doing in its Lab for Natural Language Understanding and Artificial Intelligence, according to Kaplan. "Natural language is growing and migrating beyond just voice technologies," he says. "There's a blending of natural language and artificial intelligence so that [systems] can interpret customer intentions and act on them."

To that end, a lot of work is being done around contextual understanding. "There can be ambiguity in interpreting an utterance," Kaplan states. "It relies on understanding the context, being able to match the concepts with the information available so the system comes to the right conclusions."

That, he adds, needs more information than the language itself.

"The evolution that is going on now is to have mixed-initiative dialogues," Kaplan says, noting that these interactions involve a series of collaborative questions and answers between the customer and the system. "The system should be able to reason what the customer wants and draw out other information to help him," Kaplan says.

Artificial intelligence will also be able to help guide systems when the original customer request cannot be fulfilled. This could be useful, for example, when a customer calls a restaurant to make a reservation and no tables are available at the desired time. "The system should be able to offer other suggestions around the original intent once it recognizes that the request couldn’t be fulfilled," Kaplan says.

As another example, Kaplan says customers should be able to ask for a romantic dinner and a movie on a given night and have the system know that a romantic dinner probably involves a white tablecloth and candles, that dinner probably takes about an hour and a half, what movies playing might be considered romantic, and then recommend a theater close to the restaurant. “Some of these capabilities are on the horizon. Many of the core technologies are available; it’s just a question of how to put them all together,” he says. "We're on the cusp, with a lot of new capabilities coming to make natural language better for users," he says. "It's in the lab, and it will come into products in a year or so."

It might not be Star Trek yet, but the industry is getting closer. 

News Editor Leonard Klie can be reached at lklie@infotoday.com.


NLP Market Set to Soar

Research firm MarketsandMarkets projects the natural language processing market will grow from $3.8 billion in 2013 to $9.9 billion in 2018, representing a 21.1 percent compound annual growth rate.

In the firm's current scenario, the e-commerce, healthcare, IT, financial services, and telecommunication verticals will continue to serve as the largest contributors to the natural language processing market.

Of those, the healthcare market is undergoing a higher rate of growth than other verticals, with vendors such as Nuance, 3M, and M*Modal taking the lead. Many hospitals and clinics have already adopted NLP technology to manage medical documentation and transcriptions.

In financial services, the second-largest growth segment, vendors are focusing on machine translation for cross-border payments and foreign exchanges and on virtual assistants. Companies such as Citibank and Barclays are using NLP for biometric security, and, to a limited extent, in their contact centers. In the Barclays application, for example, customers are automatically verified as they speak naturally with contact center agents; the system compares the callers’ voices with voiceprints on file.


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