Data Science

Natural Language Processing and Machine Learning to Harness the Power of Words

Udit Bansal
Thu 16 July 2015

Entire revolutions have been based on harnessing the power of words. From the Gutenberg press to Dr. King’s speech about a dream, words can reach the masses, spur them to action, and change the course of history.

Today, words hold the same, if not greater power. With digitization technology, text is abundant and more accessible than ever. Pop culture hinges on the tweets and blogs of its key players. Records ranging from the medical community to law enforcement to education reside in the cloud, making them both intangible and limitless. Entire economies shift based on customer sentiments.

The key to harnessing the power of these words is text analytics, which is now possible with the digitization of data, increased computing power of commodity hardware, cloud computing that makes storage and analytics of Big Data economical, and machine learning combined with artificial intelligence.

Natural language processing (NLP) combined with machine learning (ML) has become the tool of choice to process raw text data. Google’s “‘Google Now’” or Apple’s “‘Siri” ’ are perfect examples of the application of this technology: they use NLP to understand human language and machine learning to improve their performance over time by learning as more data is fed to them.

NLP helps us to break down text into blocks, build and/or discover the structure maintaining these blocks, assign meaning to relay emotions to machines, and bring things into perspective by infusing domain expertise. Below is an example of the “Ark Syntactic and Semantic Parsing Demo”: text is broken into congruous blocks and metadata is mined.

Machine Learning, on the other hand, makes it possible for humans to teach their binary-brained off-springs how to distinguish apple the fruit from Apple the company; what basket Chicago, Philadelphia, and Gangs of New York would fit into; and what basket Chicago, Philadelphia, and New York would fit into; a seriously daunting task!

The wealth of information locked in text streams is hard to estimate, but it is undoubtedly increasing as we discover newer methods to mine information and its applications. Here are a few e-sources of text on which we’re building powerful analytics engines:

Sources of Textual Data(non-exhaustive) Applications(non-exhaustive)

Electronic Health Records

  • Precision Medicine
  • Real-Time Monitoring
  • Auditing

Social Media Data including Twitter and Blogs

(like the one you are currently reading)

  • Topic Trends
  • Sentiment Evaluation
  • Customer Intelligence

Customer Calls and Chats

  • Increasing Net Promoter Score (NPS)
  • Improving First Call Resolution (FCR)
  • Better Targeted Marketing

Criminal Investigation Reports

  • Criminal Tracking
  • Crime Prevention
  • Policy Evaluation

Digitized Books and Literary Works

  • Improving teaching methodologies
  • Content based recommendations
  • Evolution of topics

Earnings Conference Calls

  • CXO Confidence Score
  • Improving Investment Decisions


We at Innovaccer are working towards newer and better algorithms, technologies, and systems, which can help us derive the right information from a piece of text, a powerful tool for any industry.

Learn more about us at

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