6 Real-World Examples of Natural Language Processing

Many of these smart assistants use NLP to match the user’s voice or text input to commands, providing a response based on the request. Usually, they do this by recording and examining the frequencies and soundwaves of your voice and breaking them down into small amounts of code. A direct word-for-word translation often doesn’t make sense, and many language translators must identify an input language as well as determine an output one. As we explored in our post on what different programming languages are used for, the languages of humans and computers are very different, and programming languages exist as intermediaries between the two.

For example, sarcasm, idioms, and metaphors are nuances that humans learn through experience. In order for a machine to be successful at parsing language, it must first be programmed to differentiate such concepts. These early developments were followed by statistical NLP, which uses probability to assign the likelihood of certain meanings to different parts of text. Modern NLP systems use deep-learning models and techniques that help them “learn” as they process information.

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While NLP-powered chatbots and callbots are most common in customer service contexts, companies have also relied on natural language processing to power virtual assistants. These assistants are a form of conversational AI that can carry on more sophisticated discussions. And if NLP is unable to resolve an issue, it can connect a customer with the appropriate personnel.

I say this partly because semantic analysis is one of the toughest parts of natural language processing and it’s not fully solved yet. Using these approaches is better as classifier is learned from training data rather than making by hand. The naïve bayes is preferred because of its performance despite its simplicity (Lewis, 1998) [67] In Text Categorization two types of models have been used (McCallum and Nigam, 1998) [77]. But in first model a document is generated by first choosing a subset of vocabulary and then using the selected words any number of times, at least once irrespective of order. It takes the information of which words are used in a document irrespective of number of words and order.

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Instead, the platform is able to provide more accurate diagnoses and ensure patients receive the correct treatment while cutting down visit times in the process. Human language is filled with ambiguities that make it incredibly difficult to write software that accurately determines the intended meaning of text or voice data. NLP software is challenged to reliably identify the meaning when humans can’t be sure even after reading it multiple

times or discussing different possible meanings in a group setting.

These examples illuminate the profound impact of such a technology on our digital experiences, underscoring its importance in the evolving tech landscape. To summarize, natural language processing in combination with deep learning, is all about vectors that represent words, phrases, etc. and to some degree their meanings. Prominent examples of modern NLP are language models that use artificial intelligence (AI) and statistics to predict the final form of a sentence on the basis of existing portions.

What is Natural Language Processing?

Recent years have brought a revolution in the ability of computers to understand human languages, programming languages, and even biological and chemical sequences, such as DNA and protein structures, that resemble language. The latest AI models are unlocking these areas to analyze the meanings of input text and generate meaningful, expressive output. NLP uses artificial intelligence and machine learning, along with computational linguistics, to process text and voice data, derive meaning, figure out intent and sentiment, and form a response.

Chunking refers to the process of breaking the text down into smaller pieces. The most common way to do this is by

dividing sentences into phrases or clauses. However, a chunk can also be defined as any segment with meaning

independently and does not require the rest of the text for understanding. Sentence breaking is done manually by humans, and then the sentence pieces are put back together again to form one

coherent text.

As we explore in our post on the difference between data analytics, AI and machine learning, although these are different fields, they do overlap. The concept of natural language examples of natural language processing processing dates back further than you might think. As far back as the 1950s, experts have been looking for ways to program computers to perform language processing.