Do you remember a familiar voice over the phone saying “This call may be monitored for training and quality purposes”? Well, this is quite an acquainted encounter when we call customer care service of any company. Moreover, these calls are recorded for training but the data is actually gathered to improve automated services to customers by improving natural language processing algorithms. Natural language processing is assisting businesses to descend meaning from the uncountable unstructured data available online and in call-logs.
Natural Language processing thus helps a machine to understand language spoken by humans and to derive meaning out of it. NLP also helps machines to detangle linguistic barriers like regional dialects, slang or context as opposed to its innate highly structured programming language.
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A lot of ground work has been done to continually improvise natural language algorithms and we see a great number of successful applications in this domain. We take help from our phone (Siri) to setup reminders or make a call, etc., we take help from internet to look for answers (Google Now), and we talk to car to change radio channels or guide us to a location.
Defence Advanced Research Projects Agency (DARPA) is also working in this technology to understand speech and translate from different languages to English and vice versa. Technologists are working on dissecting written language which can help to analyse people’s emotions on social media websites like, Twitter and Facebook and even predict stock market based social reactions.
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There are a lot of growth possibilities attached to businesses with Natural Language Processing. We have already citing examples of improving customer satisfaction with better machine responses over the phone. This is also extremely helpful to doctors who use software to record speech like a discussion among doctors and patients. This transcription of an event can help doctors in analysing patient data and assisting doctors in better treatment.
Eric Horvitz, Microsoft Managing Director has a digital assistant as his receptionist. This machine assistant does fairly good job and is at par with a human receptionist. Researchers are working further to make it more “human-like” so that it can initiate regular conversations with people in waiting area.
Natural Language Processing can save a lot of money and be a step ahead of the competitors by actually analysing competitors’ strategy. For Instance, calls and public speeches can be monitored and analysed to know about competitors, their prices and methodology.
Also, NLP will help in gap analysis, for instance GoDaddy, through its call centres data, concluded that iPhone users find it difficult to access their application. Hence, GoDaddy created instruction scripts for agents to help its customers. If you plan to integrate NLP in your business there are API’s which can be put to use for human – computer interaction.
We know that our computers and automated services have still not reached the perfection of “Jarvis” of the movie ‘Iron Man’ or computers of ‘Star Trek’, but Google predicts such level of sophistication can be reached in coming five years which would bridge human-machine gap.