A simple introduction to Natural Language Processing (NLP)

Today, with the Digitization of everything, 80 percent of the data being created is unstructured. Audio, video, our social footprints, the data generated from conversations between customer service reps, tons of legal document’s texts processed in financial sectors are examples of unstructured data stored in Big Data.

Organizations are turning to natural language processing (NLP) technology to derive understanding from the myriad of these unstructured data available online and in call-logs.

Natural language processing (NLP) is the ability of computers to understand human speech as it is spoken. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. For example, Machine Learning has helped computers parse the ambiguity of human language.

Apache OpenNLP, Natural Language Toolkit (NLTK), Stanford NLP are various open source NLP libraries used in a real world application.

Here are multiple ways NLP is used today:

The most basic and well known application of NLP is Microsoft Word spell checking.

Text analysis, also known as sentiment analytics is a key use of NLP.  Businesses are most concerned with comprehending how their customers feel emotionally and use that data for betterment of their service.

Email filters are another important application of NLP. By analyzing the emails that flow through the servers, email providers can calculate the likelihood that an email is spam based on its content by using Bayesian or Naive based spam filtering.

Call center representatives engage with customers to hear a list of specific complaints and problems. Mining this data for sentiment can lead to incredibly actionable intelligence that can be applied to product placement, messaging, design, or a range of other use cases.

Google and Bing and other search systems use NLP to extract terms from text to populate their indexes and to parse search queries. Google Translate applies machine translation technologies in not only translating words, but in understanding the meaning of sentences to provide a truer translation.

Many important decisions in the financial markets use NLP by taking plain text announcements, and extracting the relevant info in a format that can be factored into algorithmic trading decisions. E.g. news of a merger between companies, can have a big impact on trading decisions, and the speed at which the particulars of the merger, players, prices, who acquires whom, can be incorporated into a trading algorithm and can have profit implications in the millions of dollars.

Since the invention of the typewriter, the keyboard has long been the king of human to computer interface. But now with voice recognition via virtual assistants such as Amazon’s Alexa, Google’s Now, Apple’s Siri and Microsoft’s Cortana responding to vocal prompts and doing everything from finding a coffee shop to getting directions to your office and also tasks like turning the lights on in your home, switching the heating on etc. depending on how digitized and wired-up / smart your life and house is.

Question Answering – IBM Watson is the most prominent example of question answering via information retrieval that helps guide in various areas such as healthcare, weather, insurance etc.

It is clear that Natural Language Processing takes a very important role in new machine human interfaces. It’s an essential tool for leading-edge analytics and is the near future.


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