The field has since expanded, driven by advancements in linguistics, computer science, and artificial intelligence. Milestones like Noam Chomsky’s transformational grammar theory, the invention of rule-based systems, and the rise of statistical and neural approaches, such as deep learning, have all contributed to the current state of NLP. ChatGPT is the fastest growing application in history, amassing 100 million active users in less than 3 months. And despite volatility of the technology sector, investors have deployed $4.5 billion into 262 generative AI startups.

example of nlp

Instead of wasting time navigating large amounts of digital text, teams can quickly locate their desired resources to produce summaries, gather insights and perform other tasks. Natural language processing (NLP) is one of the most exciting aspects of machine learning and artificial intelligence. In this blog, we bring you 14 NLP examples that will help you understand the use of natural language processing and how it is beneficial to businesses. IBM equips businesses with the Watson Language Translator to quickly translate content into various languages with global audiences in mind.


For example, in “XYZ Corp shares traded for $28 yesterday”, “XYZ Corp” is a company entity, “$28” is a currency amount, and “yesterday” is a date. The training data for entity recognition is a collection of texts, where each word is labeled with the kinds of entities the word refers to. This kind of model, which produces a label for each word in the input, is called a sequence labeling model. But, as the human language evolves to include more variables, the implied intent of spoken words becomes more difficult. This is especially true in a customer service setting, where there can be a diverse customer base calling. Depending on the speaker, situation and cultural bias, words can mean different things in different contexts.

What are two example of NLP?

A few examples of NLP that people use every day are: Spell check. Autocomplete. Voice text messaging.

Despite these uncertainties, it is evident that we are entering a symbiotic era between humans and machines. Future generations will be AI-native, relating to technology in a more intimate, interdependent manner than ever before. Both of these approaches showcase the nascent autonomous capabilities of LLMs. This experimentation could lead to continuous improvement in language understanding and generation, bringing us closer to achieving artificial general intelligence (AGI). Natural language is often ambiguous, with multiple meanings and interpretations depending on the context.

Smart Assistants with speech recognition

From recommending a product to getting feedback from the customers, chatbots can do everything. As Christina Valente, a Senior Director of Product Operations explains, “before Akkio ML, projects took months-long engineering effort, costing hundreds of thousands of dollars. With Akkio, we are able to build and deploy AI models in minutes, with no prior machine learning expertise or coding.” Sign up for a free trial of Akkio and see how NLP can help your business. In 2019, there were 3.4 billion active social media users in the world.

They rely on a combination of advanced NLP and natural language understanding (NLU) techniques to process the input, determine the user intent, and generate or retrieve appropriate answers. NLP can be used to interpret free, unstructured text and make it analyzable. There is a tremendous amount of information stored in free text files, such as patients‘ medical records. Before deep learning-based NLP models, this information was inaccessible to computer-assisted analysis and could not be analyzed in any systematic way.

Voice Assistants

Take for example- Sprout Social which is a social media listening tool supported in monitoring and analyzing social media activity for a brand. The tool has a user-friendly interface and eliminates the need for lots of file input to run the system. Through social media reviews, ratings, and feedback, it becomes easier for organizations to offer results users are asking for.

The more you use predictive text, the more it will adapt to your unique speech patterns. This allows for entertaining experiments in which people will send each other statements composed completely of predictive text. Here, the parser starts with the S symbol and attempts to rewrite it into a sequence of terminal symbols that matches the classes of the words in the input sentence until it consists entirely of terminal symbols. Since V can be replaced by both, „peck“ or „pecks“,
sentences such as „The bird peck the grains“ can be wrongly permitted. Processing of Natural Language is required when you want an intelligent system like robot to perform as per your instructions, when you want to hear decision from a dialogue based clinical expert system, etc.


Auto-correct helps you find the right search keywords if you misspelt something, or used a less common name. Predictive typing helps you by suggesting the next word in the sentence. IBM has launched a new open-source toolkit, PrimeQA, to spur progress in multilingual question-answering systems to make it easier for anyone to quickly find information on the web. Mail us on [email protected], to get more information about given services. Lexical Ambiguity exists in the presence of two or more possible meanings of the sentence within a single word.

example of nlp

This opens the door for incredible insights to be unlocked on a scale that was previously inconceivable without massive amounts of manual intervention. NLP is used for other types of information retrieval systems, similar to search engines. “An information retrieval system searches a collection of natural language documents with the goal of retrieving exactly the set of documents that matches a user’s question. Using Lex, organizations can tap on various deep learning functionalities. The functionality also includes NLP and automatic speech recognition.

What is NLP?

NLP can be challenging to implement correctly, you can read more about that here, but when’s it’s successful it offers awesome benefits. Many people don’t know much about this fascinating technology, and yet we all use it daily. In fact, if you are reading this, you have used NLP today without realizing it. Microsoft ran nearly 20 of the Bard’s plays through its Text Analytics API. The application charted emotional extremities in lines of dialogue throughout the tragedy and comedy datasets. Unfortunately, the machine reader sometimes had  trouble deciphering comic from tragic.

example of nlp

One of the first and most elementary uses of natural language processing in the online world is email filters. In the beginning, there were spam filters, which looked for specific patterns of words and phrases that indicated a message was spam. On the other hand, filtering has evolved, as have early iterations of natural language processing. LLMs have demonstrated remarkable progress in this area, but there is still room for improvement in tasks that require complex reasoning, common sense, or domain-specific expertise.

Customer Service Automation

“Say you have a chatbot for customer support, it is very likely that users will try to ask questions that go beyond the bot’s scope and throw it off. This can be resolved by having default responses in place, however, it isn’t exactly possible to predict the kind of questions a user may ask or the manner in which they will be raised. Conversation analytics provides business insights that lead to better CX and business outcomes for technology companies. Conversation analytics can help energy and utilities companies enhance customer experience and remain compliant to industry regulations.

How is NLP used today?

Natural Language Processing (NLP) allows machines to break down and interpret human language. It's at the core of tools we use every day – from translation software, chatbots, spam filters, and search engines, to grammar correction software, voice assistants, and social media monitoring tools.

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