14 Natural Language Processing Examples NLP Examples

example of nlp in ai

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. Natural language processing tools rely heavily on advances in technology such as statistical methods and machine learning models. By leveraging data from past conversations between people or text from documents like books and articles, algorithms are able to identify patterns within language for use in further applications. By using language technology tools, it’s easier than ever for developers to create powerful virtual assistants that respond quickly and accurately to user commands.

example of nlp in ai

NLP is used to analyze text, allowing machines to understand how humans speak. NLP is commonly used for text mining, machine translation, and automated question answering. And companies can use sentiment analysis to understand how a particular type of user feels about a particular topic, product, etc. Companies can use sentiment analysis in a lot of ways such as to find out the emotions of their target audience, to understand product reviews, to gauge their brand sentiment, etc. And not just private companies, even governments use sentiment analysis to find popular opinion and also catch out any threats to the security of the nation. As human interfaces with computers continue to move away from buttons, forms, and domain-specific languages, the demand for growth in natural language processing will continue to increase.

The Future of API Process Development: The Role of Artificial Intelligence

Natural language understanding (NLU) and natural language generation (NLG) refer to using computers to understand and produce human language, respectively. NLG has the ability to provide a verbal description of what has happened. This is also called “language out” by summarizing by meaningful information into text using a concept known as “grammar of graphics.” This feature does not merely analyse or identify patterns in a collection of free text but can also deliver insights about a product or service performance that mimics human speech.

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As models grow larger and more sophisticated, we can anticipate even better language understanding, generation, and context management. Enhanced multilingual capabilities, reduced biases, and increased commonsense reasoning are on the horizon. It is the process of producing meaningful phrases and sentences in the form of natural language from some internal representation.

How does artificial intelligence create images from the textual description?

In addition, most NLP systems prior to the 1980s relied on intricate, handwritten rules. Machine learning (ML) techniques for language processing, however, led to a revolution in NLP beginning in the late 1980s. Decision trees, one of the original ML algorithms, provided systems of strict if-then rules that were comparable to handwritten rules already in use. Any good, profitable company should continue to learn about customer needs, attitudes, preferences, and pain points.

  • Everyone is trying to understand Natural Language Processing and its applications to make a career around it.
  • With an ever-growing number of use cases, NLP, ML and AI are ubiquitous in modern life, and most people have encountered these technologies in action without even being aware of it.
  • An IDC study notes that unstructured data comprises up to 90% of all digital information.
  • With the rise of digital communication, NLP has become an integral part of modern technology, enabling machines to understand, interpret, and generate human language.

It is a very common requirement for businesses to have IVR systems in place so that customers can interact with their products and services without having to speak to a live person. It utilizes the Transformer, a novel neural network architecture that’s based on a self-attention mechanism for language understanding. It was developed to address the problem of sequence transduction or neural machine translation. That means, it suits best for any task that transforms an input sequence to an output sequence, such as speech recognition, text-to-speech transformation, etc. It also plays a critical role in the development of AI, since it enables computers to understand, interpret and generate human language. These applications have vast implications for many different industries, including healthcare, finance, retail and marketing, among others.

First, the concept of Self-refinement explores the idea of LLMs improving themselves by learning from their own outputs without human supervision, additional training data, or reinforcement learning. A complementary area of research is the study of Reflexion, where LLMs give themselves feedback about their own thinking, and reason about their internal states, which helps them deliver more accurate answers. Topic modeling is an unsupervised learning technique that uncovers thematic structure in large collections of documents. It organizes, summarizes, and visualizes textual data, making it easier to discover patterns and trends. Although topic modeling isn’t directly applicable to our example sentence, it is an essential technique for analyzing larger text corpora.

The USM has been created for use on YouTube, specifically for closed captions. The model’s automatic speech recognition (ASR) capabilities are not limited to commonly spoken languages like English and Mandarin. Instead, it can also recognize under-resourced languages, such as Amharic, Cebuano, Assamese, and Azerbaijani, to name a few.

Speech recognition

Businesses use NLP to power a growing number of applications, both internal — like detecting insurance fraud, determining customer sentiment, and optimizing aircraft maintenance — and customer-facing, like Google Translate. Most of the time, there is a programmed answering machine on the other side. Although sometimes tedious, this allows corporations to filter customer information and quickly get you to the right representative. These machines also provide data for future conversations and improvements, so don’t be surprised if answering machines suddenly begin to answer all of your questions with a more human-like voice. In addition, there’s a significant difference between the rule-based chatbots and the more sophisticated Conversational AI. Just think about how much we can learn from the text and voice data we encounter every day.

We can easily see how Chrome, or another browser, detects the language in which a web page is written. So, if you want to work in this field, you’re going to need a lot of practice. In 2014, sequence-to-sequence models were developed and achieved a significant improvement in difficult tasks, such as machine translation and automatic summarization.

The USM comprises a collection of cutting-edge speech models with 2 billion parameters, which have been trained on 12 million hours of speech and 28 billion sentences of text, spanning over 300 languages. Since you’re acquainted with the natural language processing applications, you can now dive into the field of Natural Language Processing. To save you from the headache of searching resources online, I have listed a few wonderful courses related to natural language processing. With the help of natural language processing, recruiters can find the right candidate with much ease.

example of nlp in ai

The final step in the training pipeline involves fine-tuning the model with a small amount of supervised data on downstream tasks such as automatic speech recognition (ASR) or automatic speech translation. The project uses the Microsoft Research Paraphrase Corpus, which contains pairs of sentences labeled as paraphrases or non-paraphrases. NLP enables automatic categorization of text documents into predefined classes or groups based on their content. This is useful for tasks like spam filtering, sentiment analysis, and content recommendation. Classification and clustering are extensively used in email applications, social networks, and user generated content (UGC) platforms.

Since 2015,[21] the statistical approach was replaced by neural networks approach, using word embeddings to capture semantic properties of words. Not only are there hundreds of languages and dialects, but within each language is a unique set of grammar and syntax rules, terms and slang. When we write, we often misspell or abbreviate words, or omit punctuation. When we speak, we have regional accents, and we mumble, stutter and borrow terms from other languages. Learn why SAS is the world’s most trusted analytics platform, and why analysts, customers and industry experts love SAS. Some are centered directly on the models and their outputs, others on second-order concerns, such as who has access to these systems, and how training them impacts the natural world.

example of nlp in ai

If you have any Natural Language Processing questions for us or want to discover how NLP is supported in our products please get in touch. In the United States, most people speak English, but if you’re thinking of reaching an international and/or multicultural audience, you’ll need to provide support for multiple languages. Let us break down all of the acronyms and compare machine learning vs. AI. Just like you, your customer doesn’t want to see a page of null or irrelevant search results.

  • NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models.
  • Sites that are specifically designed to have questions and answers for their users like Quora and Stackoverflow often request their users to submit five words along with the question so that they can be categorized easily.
  • It’s been said that language is easier to learn and comes more naturally in adolescence because it’s a repeatable, trained behavior—much like walking.
  • A broader concern is that training large models produces substantial greenhouse gas emissions.
  • Deep-learning models take as input a word embedding and, at each time state, return the probability distribution of the next word as the probability for every word in the dictionary.

To help you make an informed decision, download our comprehensive guide, 8 Questions to Ask Before Selecting an Applied Artificial Intelligence Master’s Degree Program. NLP is a fast-growing niche of computer science, and it has the potential to alter the workings of many different industries. Its significance is a powerful indicator of the capabilities of AI in its pursuit to reach human-level intelligence.


If machines can learn how to differentiate these emotions, they can get customers the help they need more quickly and improve their overall experience. This application helps extract the most important information from any given text document and provides a summary of that content. Its main goal is to simplify the process of sifting through vast amounts of data, such as scientific papers, news content, or legal documentation. There are different natural language processing tasks that have direct real-world applications while some are used as subtasks to help solve larger problems.

example of nlp in ai

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Published On: November 16th, 2023 / Categories: AI News /