Combining language with technology has driven innovation in artificial intelligence for decades. With the right strategy, it can help businesses today.

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SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningCombining computing technologies with human language has become a driving force for modern-day technology.

The experience of using a smartphone, for example, wouldn’t be quite the same without the ability to pull up a map with a computerized voice navigating your next turn. Tools like Google Lens, which can translate words captured by a camera on the fly, would not be quite as impressive.

These tools represent just some of the power of natural language processing (NLP), a form of artificial intelligence that promises to have use cases far beyond smartphones.

For businesses, the ability to process speech and written words in real time could prove essential as organizations hope to better understand consumer and employee sentiment, analyze data and automate tasks that once required careful manual analysis.

Still, we may be only scratching the surface of NLP.

What Is Natural Language Processing?

At a high level, natural language processing describes a computer’s ability to process and comprehend language, whether in written, spoken or digital form.

It’s often thought of as a very recent capability of computers. In fact, however, NLP dates to the earliest days of computers. For example, early optical character recognition systems relied on specialized fonts that computers could detect. 

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Today, natural language processing is seen as mainstream and practical, with AI-powered smart assistants such as Google Assistant, Apple’s Siri, Amazon Alexa and Microsoft’s Cortana well established as mainstream use cases.

AI has become crucial in business as well, and NLP is seen as a major area of growth for many companies’ AI strategies. The Global AI Adoption Index 2021, an IBM Watson project, found that nearly half of businesses are using some form of NLP technology, with another quarter of businesses expected to use it within the next 12 months.

“The top use cases for NLP today — improving the customer experience and helping employees reach new levels of productivity — are critical priorities for nearly every business,” says Dakshi Agrawal, an IBM fellow and CTO for AI at IBM.

What Are the Steps in NLP?

The steps involved in natural language processing start with having access to data in its original form (a written message in a database, for example) and a language base to compare it with.

After the data is collected, the information is broken down using several data preprocessing techniques. Among them: […]

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