Natural language processing (NLP) is the field of study that comprises the intersection of computer science, AI, and computational linguistics. It enables computers to assess, understand, and extract meaning from  human language in a smart and useful way. Using NLP paves ways for developers to organize and structure knowledge to perform tasks, including automatic summarization, translation, person’s identification, sentiment analysis, speech recognition, and topic segmentation, among others.

Thanks to recent progresses in data access and  computational power, NLP has evolved much more, allowing professionals to derive meaningful results in areas like healthcare, finance, human resources, and others.

What is NLP used for?

NLP has a large variety of uses in almost every industry. It has the ability to automatically handle natural human languages such as speech or text. It can also help a business employee with numerous tasks, eventually bolstering work performance. Many developers typically use NLP algorithms to recapitulate blocks of text to excerpt imperative and main ideas; create chatbots to ask queries and answer appropriately;  sentiment analysis; and cognitive assistance and more.

For instance, companies like Yahoo and Google use natural language processing to filter and classify emails by assessing text in emails that flow through users’ servers and halting spam before they even enter their inbox. The majority of data or information organizations collect – be it private or public – is unstructured text, including social media conversations, comments on websites, narrative reports, and others. Deriving actionable insights from these data can be challenging.

In its effort to ease these kinds of challenges, the  Defense Advanced Research Projects Agency (DARPA) built the Deep Exploration and Filtering of Text (DEFT) program. The program uses NLP to automatically mine germane information and assist analysts to derive actionable insights from it. DEFT aims to address remaining capability gaps related to inference, causal relationships and anomaly detection.

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