Natural Language Processing in the Era of Big Data

We all grew up watching science-fiction movies where the hero interacts with machines and robots on his mission to get his ladylove held by the villains. Back then, I did not know this technology will become real, and we would be able to communicate with machines to get things done. The combination of computer science, artificial intelligence, and computational linguistics has made it possible through Natural Language Processing (NLP). The technique incorporates machine-based algorithms which have the ability to derive meaning from both verbal and written communication.

How and where is NLP Used?
NLP is a component of artificial intelligence and is widely used to recognise human speech, language translation, information retrieval and artificial intelligence. Many of us embrace NLP in our daily lives, from asking directions to our smartphones and hearing automated calls from call centers.

By leveraging this technology, organisations create new values and improve operational efficiencies. The most common applications of NLP include:

Sentiment Analysis: This analyses text to understand the sentiments behind user messages and conversations. It is often used by social analytics companies to study consumer behaviour and embark on brand strategies.

Voice Analysis and Processing: It is used to recognise the human voice. The users can automatically be identified based on their voice. Further, it allows companies to translate verbal commands into computer-based actions.

Entity Recognition: It identifies entities in the text and classifies them into different objects such as persons, organisations, and products. Google search is the best example of this application.

Syntax Analysis: It analyses the grammatical structure of texts, splits them into parts to help understand patterns emanating from it.

Automatic Translation and Summarisation: This application enables the computer to translate text from one language into another. It is further used in summarising complex texts into a short and concise piece of information.

The most common tasks based on which NLP performs the above applications include tagging of speech, tokenization (splitting text into words), classifying/grouping different entities, and creating parse trees (it creates sentence diagrams).

NLP has surpassed the language barriers between people and machines. The technology is gaining enormous traction and has huge potential for the big data and analytics industry, where the data is amassed exponentially. NLP helps in analysing the increasing volume of unstructured data including emails, messages, and voice calls, and provide insights into human behaviour.

The current approaches to NLP are based on machine learning, and it would be interesting to turn natural language to communicate with electronic devices in a seamless manner through the cloud. This is going to significantly fuel the growth of internet of things and pave the way to an era of robotics, machines and human communication with them.

The Market Potential
NLP is not a new technology. Siri, Cortana and Google Now are the known voice-controlled natural language interfaces that use NLP. Major IT firms including Google, Microsoft, IBM and Apple have piqued interest in NLP and have been researching further in this area. The technology has the potential to change the way we communicate with machines using machine learning, big data, and artificial intelligence.

According to Markets and Markets, the NLP market is estimated to grow from US$7.63 Bn in 2016 to US$16.07 Bn by 2021, at a CAGR of 16.1%. The market includes Interactive Voice Response (IVR), Optical Character Recognition (OCR), Speech Recognition, Text Processing, and Pattern & Image Recognition by products and services.

"The major forces driving the NLP market are an increase in demand for enhanced customer experience, an increase in usage of smart devices, emerging options in application areas, increased investment in the healthcare industry, increased deployment of the web & cloud-based business applications, and growth in machine-to-machine technology", added the research firm.

The Road Ahead
The development of NLP applications is challenging since text and voice contain information at many granularities from hierarchical syntactic representation to high-level logical representation. The biggest challenge now is to build effective software that will understand the information which is often ambiguous, has emotions, tone, and often lack a structure. A lot of companies are delving deep into this area to address the problems using a blend of knowledge-engineered, and statistical and machine-learning techniques to disambiguate and respond to natural human language.

These developments will ultimately help organisations to cross out the use of specialised programming languages and use pure human input for information processing. Needless to say, it will open doors to more detailed insights and effective predictive models. With the high-volume of information available today, the NLP applications will effectively process information for enhanced insights and decision making. As Carly Fiorina, Former CEO of Hewlett-Packard Said," The goal is to turn data into information, and information into Insights".