AI Strategies: What is Natural Language Processing (NLP)?


The combination of computer technologies with human language has become a driving force for modern technology.

Using a smartphone, for example, would not be quite the same without the ability to call up a map with a computerized voice that navigates the next time you turn. Tools like Google Lens, which can instantly translate words captured by a camera, wouldn’t be quite as impressive.

These tools represent only part of the power of Natural Language Processing (NLP), a form of artificial intelligence that promises use cases far beyond smartphones.

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

Still, we may just scratch the surface of NLP.

What is natural language processing?

At a high level, natural language processing describes the ability of a computer to process and understand language, whether in written, spoken or digital form.

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It is often viewed as a very new skill in computers. In fact, however, NLP dates back to the earliest days of computers. For example, early optical character recognition systems were based on specialized fonts that computers could recognize.

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

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

“The top use cases for NLP today – enhancing the customer experience and helping employees reach new levels of productivity – are critical priorities for almost any business,” said Dakshi Agrawal, IBM Fellow and CTO for AI at IBM.

What are the steps in NLP?

The natural language processing steps start with accessing data in its original form (e.g. a written message in a database) and a language base to which it can be compared.

After the data is collected, the information is broken down using various data preprocessing techniques. Among them:

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After preprocessing, the data is analyzed using a variety of AI techniques such as machine learning to infer what it means in a particular use case – such as what a customer asks when they call an automated telephone system. This information can then be processed in this use case.

Different tech companies have different approaches to AI and NLP. IBM approaches AI through a four-tier system called the AI ​​ladder. In doing so, data is collected, organized and analyzed and the findings from this data are then disseminated throughout the company.

While the technology tools are important, Agrawal emphasizes that humans should also play a role in determining the outcome of an NLP use case.

“Every job function that has to do with the AI ​​application – from the end user in the industry to the application developer to production management – should understand and have a clear idea of ​​how they can support and support AI and NLP,” he said says.

What are some of the best NLP techniques?

Agrawal notes that there are three different types of techniques used in the natural language processing disciplines:

  • Deep neural networks that can be used to model information and determine a preferred outcome in a particular use case
  • Machine learning and other traditional AI approaches that rely on the use of “training data” to make decisions based on statistical methods
  • Rules-based techniques that make decisions based on a specific set of parameters

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“Of these techniques, deep neural networks get the most attention,” he says. “However, a company that is expanding its NLP discipline should take a balanced perspective with application-specific requirements and long-term maintenance of the NLP pipelines. Ultimately, a company should choose the right technology that fits the application. “

What are examples of natural language processing?

A good use case for natural language processing for IT teams is document classification. Such a classification can be good for basic sorting of information, but it can also be used in security. As Microsoft explains in the documentation for its Azure cloud platform, NLP can help identify whether an email is spam or in some way sensitive, and offers a potential way to filter out dangerous or problematic information before it reaches end users to reach.

“The output of NLP can be used for further processing or search,” explains the company.

Another area where NLP can come in handy is in business analysis, which allows users to search for information using common expressions instead of having to adapt their wording to what the search engine or business intelligence tool understands .

Personalization is also an important use case for many companies as its use is seen as an important element in understanding customer sentiment and offering services tailored to their needs.

NLP can also help analyze large databases to gain a deeper level of intelligence for making key decisions, a use case that has a lot of potential for scaling. IBM Watson is currently used to manage an AI-driven stock index that evaluates potential investments based on an in-depth analysis of data from the largest publicly traded companies. Right now it is outperforming the S&P 500 by almost 5 percent.

NLP vs. NLU and NLG: What’s the Difference?

Natural language processing can take a variety of forms, but all are generally powered by two subsets of NLP that have similar names that are sometimes used interchangeably. However, the use cases differ significantly.

Natural Language Understanding (NLU) refers to understanding and analyzing text or speech in a way that can determine the meaning of information in real time – and even capture the emotions of the person writing it, a boon for companies that improve customer service want.

“By reviewing negative sentiment comments, companies can identify and address potential problem areas within their product or service more quickly,” says Agrawal. “NLU can also be used to identify trends in customer feedback to help resolve concerns faster, reduce churn, improve the customer experience, and increase sales.”

In many ways, the difference between NLU and Natural Language Generation (NLG) is the difference between language production and language understanding. Agrawal says that NLG “essentially enables computers to write”. Often used in chatbot applications, NLG is useful for effectively automating responses naturally and can even be combined with text-to-speech technology to enable voice conversations.

“Key benefits of NLG include automating redundant or everyday tasks and enabling a higher level of personalization on a larger scale,” added Agrawal.

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