Natural language processing (NLP) is rapidly becoming as integral to the workplace as communication itself. From chatbots to language translation and voice-activated platforms, we have watched the world embark on an artificial intelligence (AI) arms race as players large and small successfully embrace NLP technology.
NLP, also known as computational linguistics, is the combination of AI and linguistics that allows us to talk to machines as if they were human. NLP powers predictive word suggestions on our mobile devices and voice-activated assistants like Siri, Bixby and Google’s voice search. NLP is a powerful tool and plays an important role in the technology many companies produce, including my own AI-driven company, Hiretual .
While the NLP technology we rely on today has proved intuitive on many fronts, NLP has made great strides since its humble beginning 50 years ago.
The Early Days Of NLP
NLP began in the 1950s as machine translation (MT). These early MT efforts were intended to aid in code-breaking during World War II . Developers hoped MT would translate Russian into English, but results were unsuccessful. Although the translations were not successful, these early stages of MT were necessary stepping stones on the way to more sophisticated technologies.
Developed in the 1960s, ELIZA and SHRDLU are two successful tokens of early NLP. SHRDLU was primarily a language program that allowed user interaction with a block world using English terms. A user could ask the program to move or manipulate the blocks, and the computer would respond.
ELIZA, the first chatbot, could hold a very limited conversation with a user, who took on the role of a patient in a kind of psychological counseling session. ELIZA was able to simulate conversation and understanding using a pattern matching and substitution methodology.
Until the 1980s, NLP was primarily driven by complex sets of hand-written rules and parameters — a time-consuming and finicky approach for any technology. NLP was revolutionized in the late 1980s thanks to the introduction of statistical NLP and machine learning-driven algorithms for language processing. These approaches power the NLP we know today.
NLP has come a long way from MT, which would be laughable to us now, given the vast array of technology available. Today, families around the world are welcoming virtual additions like Alexa into their homes. In fact, 47.3 million U.S. adults now own a smart speaker, a platform wholly dependant on NLP for survival by intaking a user’s commands and applying algorithms to decipher language and formulate responses.
Chatbots are another implementation of NLP on the rise. They rely on NLP technology to formulate applicable responses to customer questions by analyzing the language typed into the text fields. Chatbots not only streamline incoming FAQs but also allow customers to access new information or be rerouted to relevant pages almost instantaneously, providing a value proposition on both ends of the communication. In a recent Oracle , 80% of respondents said they already used or planned to use chatbots by 2020 for consumer-facing products.
In fact, Google recently expansions to its Cloud AutoML platform, including NLP and translation, while Hearst already benefits from the technology and its ability to organize international and domestic content automatically.
Among the industries impacted by AI-based communications, talent acquisition is highly susceptible to significant disruption given its innate people-centric and communicative nature. NLP plays an important role in increasing accuracy in candidate matching from large talent pools. NLP also aids in guiding applicants using chatbots, simplifying scheduling, making accessible job descriptions, intuitive resume pairing and more. In an AI-driven world, it’s not surprising that nearly all industries are impacted by NLP.
The Future Focus Of NLP
Until now, we have interacted with computers in ways they understand, adapting to their language, rather than having them adapt to ours. But within the past few decades, they have begun to learn our language. Before long, business transforming, life-changing information will not only be discovered merely by talking with a chatbot but will impact many other parts of our lives.
While it’s certainly amazing to contemplate what’s to come, voice-activated searches and commands can still be frustratingly hit-or-miss. When they work the way the user would like, it’s because the question can be answered with highly structured data. The challenge is identifying which specific structured data to use, how to gather it and how much of it is necessary to understand and answer a human’s question.
NLP aims to eventually dominate human-to-machine interaction to the point where talking to a machine is as easy as talking to a human. NLP will continue to harness unstructured data and make it more meaningful to a machine. IDC recently forecasted that “the amount of analyzed data ‘touched’ by cognitive systems will grow by a factor of 100 to 1.4 ZB by 2025 ,” impacting thousands of industries and companies around the globe. Robotics, health care, financial services, connected auto and smart homes are just a handful of the sectors that will continue to be advanced by NLP.
Joining forces with big data, NLP will play a critical role in deriving business intelligence from raw business data, including product data, sales and marketing data, customer support, brand reputation and the current talent pool of an enterprise. This means NLP will be the key to shifting many legacy companies from data-driven to intelligence-driven platforms, helping humanity quickly get the insights they need to make decisions.
NLP has changed the way we interact with computers and machines. What started out as complicated, handwritten formulas is now a streamlined set of algorithms powered by AI. NLP technologies will be the underlying force for transformation from data-driven to intelligence-driven endeavors, as they shape and improve communication technology in the years to come.
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