Natural Language Processing NLP A Complete Guide

Text analysis solutions enable machines to automatically understand the content of customer support tickets and route them to the correct departments without employees having to open every single ticket. Not only does this save customer support teams hundreds of hours,it also helps them prioritize urgent tickets. In NLP, syntax and semantic analysis are key to understanding the grammatical structure of a text and identifying how words relate to each other in a given context. But, transforming text into something machines can process is complicated. Things like autocorrect, autocomplete, and predictive text are so commonplace on our smartphones that we take them for granted. Autocomplete and predictive text are similar to search engines in that they predict things to say based on what you type, finishing the word or suggesting a relevant one.

To better understand the applications of this technology for businesses, let’s look at an NLP example. Spellcheck is one of many, and it is so common today that it’s often taken for granted. This feature essentially notifies the user of any spelling errors they have made, for example, when setting a delivery address for an online order. Search engines leverage NLP to suggest relevant results based on previous search history behavior and user intent. NLP works through normalization of user statements by accounting for syntax and grammar, followed by leveraging tokenization for breaking down a statement into distinct components.

What Is Natural Language Generation?

Let us take a look at the real-world examples of NLP you can come across in everyday life. A smart-search feature offers the same autocomplete services as well as adding relevant synonyms in context to a catalogue to improve search results. Klevu is a company that provides smart search capability powered by NLP coupled with self-learning technology. Best suited for e-commerce portals, Klevu offers relevant search results and personalised search based on historical data on how a customer previously interacted with a product or service.

Chatbots might be the first thing you think of (we’ll get to that in more detail soon). But there are actually a number of other ways NLP can be used to automate customer service. They are effectively trained by their owner and, like other applications of NLP, learn from experience in order to provide better, more tailored assistance. Smart assistants, which examples of natural language were once in the realm of science fiction, are now commonplace. Note that to combine multiple predicates at the same level via conjunction one must introduce a function to combine their semantics. The intended result is to replace the variables in the predicates with the same (unique) lambda variable and to connect them using a conjunction symbol (and).

Messenger or chatbots

There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes. Equipped with natural language processing, a sentiment classifier can understand the nuance of each opinion and automatically tag the first review as Negative and the second one as Positive. Imagine there’s a spike in negative comments about your brand on social media; sentiment analysis tools would be able to detect this immediately so you can take action before a bigger problem arises. One of the main reasons natural language processing is so critical to businesses is that it can be used to analyze large volumes of text data, like social media comments, customer support tickets, online reviews, news reports, and more. Natural language processing (NLP) is the science of getting computers to talk, or interact with humans in human language.

Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai™, a next generation enterprise studio for AI builders. Build AI applications in a fraction of the time with a fraction of the data. Some of the most common ways NLP is used are through voice-activated digital assistants on smartphones, email-scanning programs used to identify spam, and translation apps that decipher foreign languages. MonkeyLearn is a good example of a tool that uses NLP and machine learning to analyze survey results.

But communication is much more than words—there’s context, body language, intonation, and more that help us understand the intent of the words when we communicate with each other. That’s what makes natural language processing, the ability for a machine to understand human speech, such an incredible feat and one that has huge potential to impact so much in our modern existence. Today, there is a wide array of applications natural language processing is responsible for.