Natural language processing or NLP is about helping computers understand and make sense of textual data. Examples of textual data include news articles, support tickets, emails, Tweets, Youtube comments, and research articles.

While in the academic world, NLP is all about helping computers understand human language, in the industry definition, NLP is used to reference any method for analyzing and making sense of any kind of textual data, not just language data.

Industry experts estimate that 80–90% of the data in any organization is unstructured and a vast majority is in the form of text data. Further, the amount of unstructured data in organizations is expected to grow significantly faster than structured databases. Without NLP, it would be a challenge to manage and extract value from all this unstructured text data.

How Does NLP Work?

What Applications Can You Build with NLP?

While there are many applications you can build with NLP, here are a notable few.

Text Summarization Systems

Text summarization involves automatically reading textual content and generating a readable summary of the content. The goal of text summarization is to inform users without requiring them to read every single detail. This helps improve reader productivity and helps get the gist of information across quickly.

The summary can be a paragraph of text much shorter than the original content, a single-line summary, or a set of summary phrases. For example, automatically generating a headline for a news article is an example of text summarization in action.

Conversational Agents

Sentiment Analysis Models

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