Natural Language Processing

What it is and Why it Matters?

Today’s technological advancement provides ease and convenience for many activities and processes we do on a daily basis. How easily we can say “Hey Siri” and verbalise any commands or action we want the virtual assistant to do for us. How our favorite music and video streaming sites curate songs, movies, and clips which matches our interest?


This and many more practical uses in everyday life as well as a great number of organisational benefits. Think about how many service companies measure customer feedback and measure sentiments without having to manually read, record, and tally narrative and textual feedback. All these, thanks to natural language processing.

NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment, and determine which parts are important. In general terms, NLP tasks break down language into shorter, elemental pieces, try to understand relationships between the pieces and explore how the pieces work together to create meaning.


Natural Language Processing draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding.  The increased interest in human-to-machine communications, plus an availability of big data, powerful computing, and enhanced algorithms have all contributed to its advancement.



Natural language processing includes many different techniques for interpreting human language, ranging from statistical and machine learning methods to rules-based and algorithmic approaches. We need a broad array of approaches because the text- and voice-based data varies widely, as do the practical applications.

Why is NLP important?


Large Volume of Textual Data

Today’s machines can analyze more language-based data than humans, without fatigue and in a consistent, unbiased way. Considering the staggering amount of unstructured data that’s generated every day, from medical records to social media, automation will be critical to fully analyze text and speech data efficiently.


Structuring A Highly Unstructured Data Source

Human language is astoundingly complex and diverse. We express ourselves in infinite ways, both verbally and in writing. Not only are there hundreds of languages and dialects, but within each language is a unique set of grammar and syntax rules, terms and slang.


While supervised and unsupervised learning, and specifically deep learning, are now widely used for modeling human language, there’s also a need for syntactic and semantic understanding and domain expertise that are not necessarily present in these machine learning approaches. NLP is important because it helps resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics.

NLP Applications

Everyday NLP Samples


Beyond conversing with virtual assistants like Alexa or Siri, here are a few more examples:

NLP and Text Analysis

Beyond conversing with virtual assistants like Alexa or Siri, here are a few more examples:

Given the continuous advancement in NLP and AI, we will be definitely seeing more improvement and practical uses not only for organisations but also for consumers who rely on technology to provide more efficient and useful tools to improve our way of life.

Get in touch with our management consultant on how you can leverage on Natural Language Processing to improve your business process and align technological outcome. 

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