Natural Language Processing (NLP) is a technology that enables computers to understand, contextualize and respond to human languages. As a branch of artificial intelligence, NLP is designed to make human-machine interactions easier and subtle so that smart applications like GPS or Alexa can help us run our everyday life.
Human beings communicate with each other using text and speech. This usually happens over messages, e-mails, phone calls or even voice commands on smart devices. Computers, in contrast, are not hardwired to understand the human language because they have their own language – a sequential array of 0s and 1s.
When a computer gets access to audio or pools of data, it breaks it down to algorithms which it then matches with millions of other algorithms pre-coded into it. However, when it comes to the human language, computers find it hard to match every tone of voice, inflection, slang, dialect or even abbreviations. Sometimes, the way we use grammar is also difficult for machines to decipher.
To carry out tasks where human language needs to be evaluated, NLP uses a bunch of techniques. Engineers known as Computational Linguists develop systems that can synthesize or ‘understand’ human language. As human language is based on syntax (words and phrases that form sensible sentences) and semantics (meanings of words and phrases), NLP performs an in-depth analysis on both these criteria to assess the data at hand.
Some of the syntactic analysis techniques involve word segmentation, part-of-speech tagging, stemming, parsing and breaking of sentences. While this is still easy to perform and analyse, semantic analysis isn’t. In this context, computers haven’t triumphed humans in their use of language and the ambiguous meanings most of our conversations carry.
By adopting these techniques, NLP can perform several high-end capabilities such as text-to-speech conversion, document analysis, topic categorization, sentiment analysis, machine translation, text summarization as well as generation of keyword tags, especially used while trawling social media sites for popular hashtags. It also classifies conversations and interests into groups in the same manner it categorises topics.
Our digital lives have created massive volumes of data that has made segmentation an imperative requirement. NLP not only converts hardcovers into e-books, it also screens data to highlight trends on the internet and gather real-time insights concerning the weather or traffic or even medical records of patients of a particular demographic.
By cutting out lag, NLP offers statistical inferences from huge data chunks in just a matter of seconds. In businesses as well as social media, NLP can mine user sentiment and give an accurate analysis of how exactly users respond to frequently-asked questions or what they think of events or happenings. Interactive voice response applications such as telecalling and virtual assistants like Siri also use NLP to gather valuable intelligence on browsing or buying patterns.
IBM was one of the pioneers to develop NLP through ‘Watson’, its signature AI platform that assists employees with value-driven industry insights. Today, NLP drives functionalities and applications across sectors and among enterprises of all types and sizes. NLP continues to influence healthcare, education, pharmaceuticals, banking and investment, manufacturing, insurance and legal sectors.
By filtering information from stockpiles of unstructured data and highlighting measurable impact, NLP can offer quick and accurate decision-making. In healthcare, it helps patients with cardio-vascular diseases detect stroke symptoms early on as well as determine what medical intervention can be applied to people depending on their risk assessments.
Investment banks also depend on NLP to analyse customer behaviour on investment patterns and loan requirements. Finance and law firms employ NLP solutions to compare data, classify dossiers and format deals to make them legally compliant. In manufacturing, NLP decreases human intervention to account for faster production and nip assembly-line glitches in real time.
In India, where the size of internet users is projected to rise to 829 million or 59 per cent of the population by this year, businesses are cashing on NLP in a huge way to target customers via advertising, social media and multi-language services. E-commerce sites and multi-media are exploiting NLP’s translation capabilities to serve the country’s enormous non-English speaking population.
According to Deloitte, India’s workforce is estimated to grow from 473 million in 2018 to 600 million in 2022. This figure demands a strong parallel to the kind of skills required for the Future of Work. The World Economic Forum’s ‘The Future of Jobs 2018’ report suggests that while automation is likely to displace 75 million jobs, it is poised to create a whopping 133 million new ones instead.
This has opened more opportunities in NLP powered jobs such as data scientists, search engine analysts, testing managers and computational linguists. Increasing demand for NLP-focussed applications have spurred India’s research scope in in the field as well. Institutions like the IITs, IISc, School of Computer & Systems Sciences at JNU and School of Computer & Information Sciences at University of Hyderabad offer multiple research projects centering NLP.
The Centre for Development of Advanced Computing (C-DAC) under the Ministry of Electronics and Information Technology builds top-notch NLP applications under GIST Labs, to broaden R&D and facilitate the availability of content in a majority of Indian languages on the web.
On the innovation front, INDIAai, a joint initiative of Ministry of Electronics and Information Technology (MeitY), National E-Governance Division (NeGD) and National Association of Software and Service Companies (NASSCOM) is currently working on a suite of NLP technologies piloted by scientists from Microsoft India Development Center and Microsoft Research India.
Policy think tank Niti Aayog, in its recognition for the need to develop research ecosystems, also has structured its flagship #AIForAll strategy to build R&D and promote skills. It focuses on AI influence in sectors like healthcare, education and agriculture. The agency hopes to enhance and empower human capabilities within the sector and work on scalable solutions for developing economies.
According to data company Gradient Flow’s 2020 NLP survey report, NLP budgets, libraries and cloud services were seen to be soaring. Conducted among 571 respondents across 50 countries, the firm’s report showed that 53% of respondents who were technical leaders stated their NLP budget was at least 10% higher compared to 2019.
This isn’t quite surprising as NLP holds commanding influence in all sectors focusing on automation and data processing. Some of the key NLP trends to watch out for include the rise of
Multilingual NLP, Market Intelligence Tracking, Supervised & Unsupervised Machine learning, Scam & Phishing Detection, Online Customer Service, Super Chatbots, Language models like BERT programmed to understand meaning and context, Transfer Learning and Talent Acquisition.
