14 Natural Language Processing Examples NLP Examples

The first chatbot was created in 1966, thereby validating the extensive history of technological evolution of chatbots. The monolingual based approach is also far more scalable, as Facebook’s models are able to translate from Thai to Lao or Nepali to Assamese as easily as they would translate between those languages and English. As the number of supported languages increases, the number of language pairs would become unmanageable if each language pair had to be developed and maintained. Earlier iterations of machine translation models tended to underperform when not translating to or from English. Natural language processing has been around for years but is often taken for granted.

So a document with many occurrences of le and la is likely to be French, for example. When companies have large amounts of text documents (imagine a law firm’s case load, or regulatory documents in a pharma company), it can be tricky to get insights out of it. For years, trying to translate a sentence from one language to another would consistently return confusing and/or offensively incorrect results. This was so prevalent that many questioned if it would ever be possible to accurately translate text. Data analysis has come a long way in interpreting survey results, although the final challenge is making sense of open-ended responses and unstructured text.

NLG vs. NLU vs. NLP

As a Gartner survey pointed out, workers who are unaware of important information can make the wrong decisions. NLP customer service implementations are being valued more and more by organizations. Owners of larger social media accounts know how easy it is to be bombarded with hundreds of comments on a single post. It can be hard to understand the consensus and overall reaction to your posts without spending hours analyzing the comment section one by one. These devices are trained by their owners and learn more as time progresses to provide even better and specialized assistance, much like other applications of NLP.

• Levity is a tool that allows you to train AI models on images, documents, and text data.
• This is infinitely helpful when trying to communicate with someone in another language.
• And though increased sharing and AI analysis of medical data could have major public health benefits, patients have little ability to share their medical information in a broader repository.
• The “bag” part of the name refers to the fact that it ignores the order in which words appear, and instead looks only at their presence or absence in a sentence.

Although rule-based systems for manipulating symbols were still in use in 2020, they have become mostly obsolete with the advance of LLMs in 2023. In the past years, she came up with many clever ideas that brought scalability, anonymity and more features to the open blockchains. She has a keen interest in topics like Blockchain, NFTs, Defis, etc., and is currently working with 101 Blockchains as a content writer and customer relationship specialist. Examples include novels written under a pseudonym, such as JK Rowling’s detective series written under the pen-name Robert Galbraith, or the pseudonymous Italian author Elena Ferrante. In politics we have the anonymous New York Times op-ed I Am Part of the Resistance Inside the Trump Administration, which sparked a witch-hunt for its author, and the open question about who penned Dominic Cummings’ rose garden statement. But the combination sch is common only in German and Dutch, and eau is common as a three-letter sequence in French.

What Is Natural Language Understanding (NLU)?

Here are eight examples of applications of natural language processing which you may not know about. If you have a large amount of text data, don’t hesitate to hire an NLP consultant such as Fast Data Science. A chatbot system uses AI technology to engage with a user in natural language—the way a person would communicate if speaking or writing—via messaging applications, websites or mobile apps. The goal of a chatbot is to provide users with the information they need, when they need it, while reducing the need for live, human intervention. The information that populates an average Google search results page has been labeled—this helps make it findable by search engines. However, the text documents, reports, PDFs and intranet pages that make up enterprise content are unstructured data, and, importantly, not labeled.

Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment. Natural language processing can be an extremely helpful tool to make businesses more efficient which will help them serve their customers better and generate more revenue. As these examples of natural language processing showed, if you’re looking for a platform to bring NLP advantages to your business, you need a solution that can understand video content analysis, semantics, and sentiment mining.

And while applications like ChatGPT are built for interaction and text generation, their very nature as an LLM-based app imposes some serious limitations in their ability to ensure accurate, sourced information. Where a search engine returns results that are sourced and verifiable, ChatGPT does not cite sources and may even return information that is made up—i.e., hallucinations. At the intersection of these two phenomena lies natural language processing examples of natural languages (NLP)—the process of breaking down language into a format that is understandable and useful for both computers and humans. Still, as we’ve seen in many NLP examples, it is a very useful technology that can significantly improve business processes – from customer service to eCommerce search results. Optical Character Recognition (OCR) automates data extraction from text, either from a scanned document or image file to a machine-readable text.

We were blown away by the fact that they were able to put together a demo using our own YouTube channels on just a couple of days notice. Repustate has helped organizations worldwide turn their data into actionable insights. Learn how these insights helped them increase productivity, customer loyalty, and sales revenue. In order to streamline certain areas of your business and reduce labor-intensive manual work, it’s essential to harness the power of artificial intelligence. People go to social media to communicate, be it to read and listen or to speak and be heard.

Advantages and Disadvantages of Artificial Intelligence

Also, the structure is very

important, so it is usually not a good idea to read from top to bottom, left to

right. Instead, learn to parse the program in your head, identifying the tokens

and interpreting the structure. Little things

like spelling errors and bad punctuation, which you can get away with in

natural languages, can make a big difference in a formal language. Transformer models take applications such as language translation and chatbots to a new level. Innovations such as the self-attention mechanism and multi-head attention enable these models to better weigh the importance of various parts of the input, and to process those parts in parallel rather than sequentially. Natural language processing is a branch of AI that enables computers to understand, process, and generate language just as people do — and its use in business is rapidly growing.