How Machine Learning Is Saving The Indian Vernacular ?

November 18, 2018
Machine Learning Courses

 

In a nation riddled with countless cultures, unending dialects and infinite separations, the term ‘melting pot’ comes to mind. It’s common for the typical Indian being confused with the local tongues when treading into unfamiliar territories.

Fortunately for the millions of Indians beguiled by such problems, machine learning courses and a number of data science tools is proving to be a much-needed relief for preserving and keeping those languages intact.

Connecting Data To Language

Big Data

This has significantly boosted the outlook for interdisciplinary research that has allowed researchers across the country to link the aspects of linguistics and fragment all dialects to a condensed format that can be edited easily.Until now, several companies have taken to using an aggregator system to create a platform that translates the language into any other without sacrificing minor details. Several years ago, a research project under the name Technology Development for Indian Language was created by the government to scrape all the major Indian languages for data science purposes.

  • One such platform that has been making strides is the e-Bhasha platform that is making content available for citizens in their language. It was created as a big data project in 2015 and has become a starting point for many linguistic researchers.
  • As the number of internet users in India grew more than 28 per cent and is expected to be a $6.2 billion industry per year, international groups are jumping on the bandwagon to appeal to the common man.

Playing With The Locals

Seeing the enormous benefits of tapping into local consumers, big groups like Google set out to create the Google Brain which is essentially an extensive neural network to develop human language from the get-go.

  • Aspects of this have been incorporated into Google Assistant as well, having translated content from more than 500 million monthly users and 140 billion words per day in as many 158 languages.
  • The craze began by the year 2013 when e-commerce was still taking root in the country and was challenged by the numerous languages that consumers had in the country.
  • Websites like Flipkart and Snapdeal dealt with local language content for mobile websites as far back as 2015.
  • Reports suggest that Marathi, Gujarati, Tamil, Punjabi and Malayalam represented over 75 per cent of searches on Google in the very same languages. What’s even more interesting is that more than 73% of people surveyed are willing to go completely digital if the system communicates in their own language.   
  • Facebook has raised the number of Indian languages for posting to almost 12 but still lacks regional pages that use the same kind.
  • Small firms in India are collecting as much textual Corpus for languages available using translation services like Reverie, Process9 and IndusOS.  

The Technology Used

  • Most companies would confess to the use of neural networks for developing such programs, but the primary machines behind such global endeavors has been some rather sophisticated algorithms.
  • The newest additions to the industry happen to be some enhanced versions of the Hadoop MapReduce extension. A significant feature of the software is the ability to find linguistic linkers between similar words and compound phrases which makes translations more concrete. Some stellar packaged additions to the SPSS Modeler system too have taken place that is helping companies handle large corpuses.
  • At the same time, marketing groups are using modified techniques to feed invoice data collected from average consumers which are being sent into what’s being called a ‘global corpus data set.’
  • Likewise, teams across the country in data collection firms are hiring data collection engineers to converse and accumulate conversational audio recordings both in rural and urban areas.
  • The main subject remains heavily invested in cross-directional neural networks many of which are using data analysis tools and machine learning tools like Tensor Flow from Google and IBM Watson.

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