What is Natural Language Processing?
Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and manipulate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding.
While natural language processing isn’t a new science, the technology is rapidly advancing thanks to an increased interest in human-to-machine communications, plus an availability of big data, powerful computing and enhanced algorithms.
While we, as humans, may be able to understand and speak more than one language, most people do not have fluency in “machine code” or “machine language” – millions of ones and zeros that produce logical actions. In the past, early programmers would use punch cards to communicate with computers but now “Siri, what is the temperature today” triggers a real-time response in a human voice. This interaction is made up of multiple sections: device activation from your voice, an understanding of your command, and a response in a fluent human language. This common “conversation” is made possible by NLP thanks to machine learning and deep learning elements.
Common Uses for Natural Language Processing in Business
Chatbots are one of the more recent NLP examples. Most business bots are used in Human Resources. Some NLP tools are used to deal with common employee questions, for example, “how many days leave do I have” or even deal with workplace satisfaction requests such as “we need more milk in the breakroom”. Other chatbots are used to improve employee retention and morale – employee chatrooms are monitored by bots and when employees use certain complementary words like “kudos” or “cheers” then staff get rewarded.
Other non-bot tools are voice-activated. For example, tools are being used to listen in on company meetings for trigger phrases like “what are” and “I wonder.” When it hears them, a search function activates and retrieves an answer. It would work something like this: “What was the ROI on that last year?” Silently, the tool would scan company financials and display the results on a screen in the room. This can save the 30% of the day that employees spend searching for information and up to US $14,209 per person per year.
On the topic of HR, NLP software has long helped hiring managers sort through resumes.
By using the same techniques as Google search, automated candidate sourcing tools scan applicant CVs to pinpoint people with the required background for a job. This is difficult however, as humans we try to stand out from the crowd and may use creative terms to describe our skills and this can cause CVs to be ignored by NLP scans. More recently, NLP technology has branched to include synonym searching on keywords. In addition to this, as women and minorities use language differently, this synonym-searching keeps qualified candidates from slipping between the cracks.
Natural Language Processing (NLP) technologies are increasingly active in the labour-intensive world of call centres. Both inbound call centres (usually administer customer and product support or information enquiries) and outbound call centres (used for telemarketing, debt collection, soliciting charitable etc.) are implementing NLP tools.
The technology used in call centres roughly falls into four categories: real-time speech recognition; intent analysis, which classifies conversations based on context to predict customer intent in real time; conversation management, which ensures simultaneous processing of multi-pass conversations; and conversational analysis, which broadly analyses users’ dialogue. These previously extensively human-intense operations (demanding training, time, and money) are now more efficient and effective – thanks to NLP.
These are only a few examples of ways in which Natural Language Processing can be implemented in business situations every day. While such Artificial Intelligence technology may seem to pose a threat to employee’s jobs; this is not the case. At closer inspection, these tools are simply allowing employees to work more efficiently by having the tedious and time-consuming aspects of their roles sorted out by machines and bots. Thus, ensuring that time spent is well-spent, that employees are satisfied, customers are being heard, and ultimately, businesses save time and money!