You get me? How NLP is empowering automation

Artificial intelligence (AI) is taking the world by storm. From retail to manufacturing, AI is impacting the modern world in all manner of positive ways – and natural language processing (NLP) is playing an increasingly significant role in converting human requests into automation actions.

NLP refers to a digital program’s ability to understand human language the way we actually speak it. Latest techniques in NLP are based around deep learning, a variant of AI that gathers and analyses data patterns to enhance a program’s comprehension. However, to utilize the deep learning approach requires a great deal of data for analysis.

NLP – why has it come to the fore?

Since the 1970s the keyboard and later the mouse served as the principal human/computer interface mechanism, until touch interfaces became ubiquitous in the 2000s.

Today however, that is changing: AI-powered virtual assistants like Amazon Alexa, Google Now, Apple’s Siri and Microsoft’s Cortana are helping to take voice commands into the mainstream. Now ordering a pizza online, getting directions to somewhere or turning on the heating in your home can all be done using vocal prompts.

NLP in today’s world

Chatbots are one application where NLP has already made its presence felt. Conversational commerce is the intersection of messaging apps and shopping where consumers can chat with a company representative, ask questions and get personalized recommendations. It is another area where NLP is delivering significant advances. Building NLP into apps enables companies to scale and give users a more seamless, tailored experience.

Real world examples of this seamless conversational experience continue to emerge, such as taxi provider Uber teaming up with Facebook Messenger to allow customers to order an Uber driver without leaving the messaging app – that is, “the conversation”. Amazon’s voice-activated Echo assistant has partnered with bank Capital One, allowing customers to carry out their banking by speaking to their Echo. NLP is present in both. So it was perhaps not surprising that Facebook acquired wit.ai, a start-up provider of APIs for building voice-activated. One of the key benefits that wit.ai boasts is that its tools not only enable chatbots with NLP to help them better understand humans’ requests, but that it also helps pre-empt questions and uncover unforeseen needs.

Context is key

NLP has the power to drive better context and reasoning in AI. Until fairly recently, NLP techniques were generally driven by computational statistics – methods by which text was simply converted into data, which was then analyzed and patterns drawn from it. This approach removed context and meaning, but to truly exploit the power of AI, NLP is required.

NLP in the enterprise world

There are a number of areas that will likely see an increase of NLP applied to improve them. Pre-emptively formulating responses to questions is a key area, where customer-facing activities can be enhanced by enterprises using NLP algorithms. With today’s focus on customer experience, the ability to exponentially improve customer service and administrative activities via a data-driven approach could be essential. Customers will be able to ask questions in their native language or dialect and receive instant, accurate answers from virtual assistants.

NLP will also have a central role to play in social media monitoring. As more companies engage in social interaction with customers, with a view to understanding what they are talking about, NLP can give much greater understanding. By engaging with customers on social media and in their own vernacular, companies will be able to improve relationships with customers and better appreciate their needs.

Enterprises are already using Big Data analytics to analyze the vast quantities of data they generate and manage, and NLP could play a growing role here too. By using grammatical, syntactic and semantic analysis of language, companies will be able to identify and extract greater levels of information: topics, tones of voice, opinions and more. This in turn can be used as metadata for more accurately categorizing and marketing products, services or content.

As the lines between humans and machines continue to blur, NLP will be vital in helping enterprises deliver enhanced services and customer experiences. For as long as humans continue to speak in ways that computers do not, with tone, emotion and regional nuances, NLP will play a key role.

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