Type to SearchView Tags
Ved Parkash Pati

The Future of Data Sciences: How Deep Learning will augment NLP
Ved Parkash Pati Business Manager, Digital & Analytics | June 6, 2019

Computing systems and languages go way back in time. In fact, they can be traced back as early as the 1950s when programmers had just begun experimenting with simple language instructions to train computers to perform tasks. A decade later, natural language processing (NLP) came into being— this development was out of an effort to get computers to comprehend comparatively complicated language inputs.

Analyzing the Current State of Technological Advancement

Today, big data and analytics can parse, organize, and provide insights from large volumes of information at ease, which otherwise takes considerable manual effort. In our pursuits to understand machine learning (ML) better and improve the current advancements that NLP has taken, we have come to realize that computing comprehension of language data is extremely complicated.

Finding patterns in numerical data and statistics is a rather easy task. Understanding language, on the other hand, requires expertise over syntax, ability to distinguish between written and spoken forms, context comprehension and definitions, recognition of subtleties such as sarcasm, intent of a body of content, and shifting language patterns.

Traversing from NLP to NLU and NLG

As it stands today, NLP is limited to the basic processing of straightforward, routine queries where a bot simply parses the terms, looks at the sentences, and searches a knowledge base to find canned responses and produce them as is. Ergo, an NLP-powered chatbot on your banking application might be able to let you know the amount of personal loan you are eligible for. However, if you were to ask what amount of loan the bank would approve for a trip to the Maldives in the month of June, the natural language processing-powered bot, in all probability, would go silent.

This is where deep learning capabilities of natural language understanding (NLU) and natural language generation (NLG) come into being. Sans syntax, semantics, and pragmatics, NLP can hardly make its mark in business functions.

In ways more than one, 2018 happened to be a watershed year for NLP. Research indicates that the market for natural language processing is currently registering a compounded annual growth rate (CAGR) of 16.1% and will be valued at USD 16.07 billion by the end of 2021.

This does not come across as a matter of surprise. Close to 50% of American households own at least one smart speaker. This is indicative of a major uptake in the adoption of Siri, Alexa, and Google Home. That being said, with more than 40% of big businesses looking to adopt NLP-powered chatbots by the end of 2019, NLP alongside NLU and NLG are admittedly on the cusp of bursting through the seams in the near future.

From Information Retrieval to Response Generation

Data sciences are increasingly gearing toward a direction where the industry is keen on undoing the current lag in the adoption of NLU and NLG. In this regard, algorithmic advances that enable machines to understand contextual queries through long short term memory (LSTM) cells will be frontrunners. The successful adoption of the same will enable the provision of speedy and intelligent, human-like responses, invalidating current trends of regurgitated canned responses from a predefined knowledge base.

Data sciences are increasingly gearing toward a direction where the industry is keen on undoing the current lag in the adoption of NLU and NLG.

As we migrate from traditional feed-forward neural networks to recurrent ones, deep neural networks will increasingly become sharper and richer. Deep learning-rich techniques have already helped the data sciences industry achieve major technological advancements. By creating a symbiotic relationship between sophisticated computational linguistics and sequential pattern prediction, machines are facilitating information generation for chatbots that provide context-rich responses that comprehensively answers questions. One can only imagine what sustained development in data sciences can yield.

Do you think advancements in deep learning will amplify the future of NLP? Tell us in the comments section below.

  1. Natural Language Processing Market worth 16.07 Billion USD by 2021, MarketsAndMarkets
  2. Ibid.
  3. Nearly half of U.S. homes will have a smart speaker by year’s end, Adobe says, MarketWatch
  4. Spiceworks Study Reveals 40 Percent of Large Businesses Will Implement Intelligent Assistants or Chatbots by 2019, Spiceworks

In our next blog stay tuned to know how HCL Digital Process Operations can help you embrace deep learning