Most Popular Applications of Natural Language Processing

It couldn’t be trusted to translate whole sentences, let alone texts. Through NLP, computers don’t just understand meaning, they also understand sentiment and intent. They then learn on the job, storing information and context to strengthen their future responses. The NLP pipeline comprises a set of steps to read and understand human language.

CallMiner is trusted by the world’s leading organizations across retail, financial services, healthcare and insurance, travel and hospitality, and more. 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.

They use high-accuracy algorithms that are powered by NLP and semantics. NLP can be used to great effect in a variety of business operations and processes to make them more efficient. One of the best ways to understand NLP is by looking at examples of natural language processing in practice. Publishers and information service providers can suggest content to ensure that users see the topics, documents or products that are most relevant to them.

The Ultimate Guide to Democratization in Artificial Intelligence

We’ve all used predictive text while typing on a smartphone keyboard. It’s one of the most widely used NLP applications in the world, with Google alone processing more than 40 billion words per day. 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.

A slightly more sophisticated technique for language identification is to assemble a list of N-grams, which are sequences of characters which have a characteristic frequency in each language. For example, the combination ch is common in English, Dutch, Spanish, German, French, and other languages. An NLP system can look for stopwords (small function words such as the, at, in) in a text, and compare with a list of known stopwords for many languages.

Smart search is another tool that is driven by NPL, and can be integrated to ecommerce search functions. This tool learns about customer intentions with every interaction, then offers related results. If you’re not adopting NLP technology, you’re probably missing out on ways to automize or gain business insights.

Chatbots

In turn, this allows them to make improvements to their offering to serve their customers better and generate more revenue. Thus making social media listening one of the most important examples of natural language processing for businesses and retailers. NLP is an AI methodology that combines techniques from machine learning, data science and linguistics to process human language.

The Natural language Processing Development process is enhancing its limits. The graph below shows the increase of the NLP market in the upcoming years. As more advancements in NLP, ML, and AI emerge, it will become even more prominent. And companies can use sentiment analysis to understand how a particular type of user feels about a particular topic, product, etc.

But our NLP model doesn’t know what pronouns mean because it only examines one sentence at a time. Instead, they are using the context of how a word appears in the sentence and a statistical model to guess which type of noun a word represents. A good NER system can tell the difference between “Brooklyn Decker” the person and the place “Brooklyn” using context clues.

Both sentences talk about the noun pony, but they are using different inflections. When working with text in a computer, it is helpful to know the base form of each word so that you know that both sentences are talking about the same concept. Otherwise the strings “pony” and “ponies” look like two totally different words to a computer. When you use a concordance, you can see each time a word is used, along with its immediate context. This can give you a peek into how a word is being used at the sentence level and what words are used with it.

POS tagging is useful in many areas of NLP, including text-to-speech conversion and named-entity recognition (to classify things such as locations, quantities, and other key concepts within sentences). Focusing on natural language programming examples topic modeling and document similarity analysis, Gensim utilizes techniques such as Latent Semantic Analysis (LSA) and Word2Vec. This library is widely employed in information retrieval and recommendation systems.

This strategy lead them to increase team productivity, boost audience engagement and grow positive brand sentiment. The basketball team realized numerical social metrics were not enough to gauge audience behavior and brand sentiment. They wanted a more nuanced understanding of their brand presence to build a more compelling social media strategy. For that, they needed to tap into the conversations happening around their brand. Here are five examples of how brands transformed their brand strategy using NLP-driven insights from social listening data.

With the recent focus on large language models (LLMs), AI technology in the language domain, which includes NLP, is now benefiting similarly. You may not realize it, but there are countless real-world examples of NLP techniques that impact our everyday lives. They then use a subfield of NLP called natural language generation (to be discussed later) to respond to queries. As NLP evolves, smart assistants are now being trained to provide more than just one-way answers. They are capable of being shopping assistants that can finalize and even process order payments.

Automatic summarization is a lifesaver in scientific research papers, aerospace and missile maintenance works, and other high-efficiency dependent industries that are also high-risk. First, the capability of interacting with an AI using human language—the way we would naturally speak or write—isn’t new. Smart assistants and chatbots have been around for years (more on this below). 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.

Predictive Text Analysis

Getting started with one process can indeed help us pave the way to structure further processes for more complex ideas with more data. Ultimately, this will lead to precise and accurate process improvement. Regardless of the data volume tackled every day, any business owner can leverage NLP to improve their processes. The tools will notify you of any patterns and trends, for example, a glowing review, which would be a positive sentiment that can be used as a customer testimonial. Owners of larger social media accounts know how easy it is to be bombarded with hundreds of comments on a single post.

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. 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. The implementation was seamless thanks to their developer friendly API and great documentation. Whenever our team had questions, Repustate provided fast, responsive support to ensure our questions and concerns were never left hanging.

In layman’s terms, a Query is your search term and a Document is a web page. Because we write them using our language, NLP is essential in making search work. Any time you type while composing a message or a search query, NLP helps you type faster. 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. Georgia Weston is one of the most prolific thinkers in the blockchain space.

• Natural language processing example projects its potential from the last many years and is still evolving for more developed results.
• 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.
• Natural language processing is used when we want machines to interpret human language.
• But our NLP model doesn’t know what pronouns mean because it only examines one sentence at a time.
• Poor search function is a surefire way to boost your bounce rate, which is why self-learning search is a must for major e-commerce players.
• Spellcheck is one of many, and it is so common today that it’s often taken for granted.

For example, the words “helping” and “helper” share the root “help.” Stemming allows you to zero in on the basic meaning of a word rather than all the details of how it’s being used. NLTK has more than one stemmer, but you’ll be using the Porter stemmer. When you use a list comprehension, you don’t create an empty list and then add items to the end of it. Stop words are words that you want to ignore, so you filter them out of your text when you’re processing it.

Real-World Examples of Natural Language Processing (NLP)

And we’ll also treat punctuation marks as separate tokens since punctuation also has meaning. We’ll break down the process of understanding English into small chunks and see how each one works. Democratization of artificial intelligence means making AI available for all…

These summarization applications based on NLP can help you to summarize any text and paragraphs. This application is helpful for school and college-going students to understand any long text. Also, these automatic summarizations can be used in scientific research papers. Their basic work is they will collect the text or paragraph, paraphrase it, and then change it to a unique version of the same sentences. Natural language processing in AI expanded its range in every industry. If someone is searching for the same query repeatedly, then that information will be saved in the cache for further searches.

Semantic search powers applications such as search engines, smartphones and social intelligence tools like Sprout Social. One of the annoying consequences of not normalising spelling is that words like normalising/normalizing do not tend to be picked up as high frequency words if they are split between variants. For that reason we often have to use spelling and grammar normalisation tools. Microsoft has explored the possibilities of machine translation with Microsoft Translator, which translates written and spoken sentences across various formats. Not only does this feature process text and vocal conversations, but it also translates interactions happening on digital platforms. Companies can then apply this technology to Skype, Cortana and other Microsoft applications.

The misspelled word is then added to a Machine Learning algorithm that conducts calculations and adds, removes, or replaces letters from the word, before matching it to a word that fits the overall sentence meaning. Then, the user has the option to correct the word automatically, or manually through spell check. Request your free demo today to see how you can streamline your business with natural language processing and MonkeyLearn.

How Are NLP Tools from Microsoft, Google & Apple Making World Hands-Free?

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. There’s also some evidence that so-called “recommender systems,” which are often assisted by NLP technology, may exacerbate the digital siloing effect.

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. Employee-recruitment software developer Hirevue uses NLP-fueled chatbot technology in a more advanced way than, say, a standard-issue customer assistance bot. In this case, the bot is an AI hiring assistant that initializes the preliminary job interview process, matches candidates with best-fit jobs, updates candidate statuses and sends automated SMS messages to candidates. Because of this constant engagement, companies are less likely to lose well-qualified candidates due to unreturned messages and missed opportunities to fill roles that better suit certain candidates. From translation and order processing to employee recruitment and text summarization, here are more NLP examples and applications across an array of industries.

But how would NLTK handle tagging the parts of speech in a text that is basically gibberish? Jabberwocky is a nonsense poem that doesn’t technically mean much but is still written in a way that can convey some kind of meaning to English speakers. So, ‘I’ and ‘not’ can be important parts of a sentence, but it depends on what you’re trying to learn from that sentence.

Not only that, but when translating from another language to your own, tools now recognize the language based on inputted text and translate it. As a diverse set of capabilities, text mining uses a combination of statistical NLP methods and deep learning. With the massive growth of social media, text mining has become an important way to gain value from textual data. Other connectionist methods have also been applied, including recurrent neural networks (RNNs), ideal for sequential problems (like sentences).

Let’s take the idea of detecting entities and twist it around to build a data scrubber. Let’s say you are trying to comply with the new GDPR privacy regulations and you’ve discovered that you have thousands of documents with personally identifiable information in them like people’s names. You’ve been given the task of removing any and all names from your documents. Notice that it makes a mistake on “Londinium” and thinks it is the name of a person instead of a place. This is probably because there was nothing in the training data set similar to that and it made a best guess. Named Entity Detection often requires a little bit of model fine tuning if you are parsing text that has unique or specialized terms like this.

A lot of the data that you could be analyzing is unstructured data and contains human-readable text. Before you can analyze that data programmatically, you first need to preprocess it. In this tutorial, you’ll take your first look at the kinds of text preprocessing tasks you can do with NLTK so that you’ll be ready to apply them in future projects. You’ll also see how to do some basic text analysis and create visualizations.

It leverages the Transformer neural network architecture for comprehensive language understanding. BERT is highly versatile and excels in tasks such as speech recognition, text-to-speech transformation, and any task involving transforming input sequences into output sequences. It demonstrates exceptional efficiency in performing 11 NLP tasks and finds exemplary applications in Google Search, Google Docs, and Gmail Smart Compose for text prediction.

The deep neural network learns the structure of word sequences and the sentiment of each sequence. Given the variable nature of sentence length, an RNN is commonly used and can consider words as a sequence. A popular deep neural network architecture that implements recurrence is LSTM. NLP models such as neural networks and machine learning algorithms are often used to perform various NLP tasks.

According to The State of Social Media Report ™ 2023, 96% of leaders believe AI and ML tools significantly improve decision-making processes. Natural language processing with Python is helpful when it comes to predicting text. You might see this while composing an email after adding the email of the person to whom you want to send the email. NLP Development Services are of diverse types such as summarization, text generation from speech, conversion of speech into text, etc. Here, in this blog, we will be discussing 10 such applications of NLP.

At the intersection of these two phenomena lies natural language processing (NLP)—the process of breaking down language into a format that is understandable and useful for both computers and humans. By performing sentiment analysis, companies can better understand textual data and monitor brand and product feedback in a systematic way. These are the most common natural language processing examples that you are likely to encounter in your day to day and the most useful for your customer service teams. Natural language capabilities are being integrated into data analysis workflows as more BI vendors offer a natural language interface to data visualizations.

With an AI-platform like MonkeyLearn, you can start using pre-trained models right away, or build a customized NLP solution in just a few steps (no coding needed). Text extraction, or information extraction, automatically detects specific information in a text, such as names, companies, places, and more. You can foun additiona information about ai customer service and artificial intelligence and NLP. You can also extract keywords within a text, as well as pre-defined features such as product serial numbers and models. Natural language processing tools can help businesses analyze data and discover insights, automate time-consuming processes, and help them gain a competitive advantage.

More broadly speaking, the technical operationalization of increasingly advanced aspects of cognitive behaviour represents one of the developmental trajectories of NLP (see trends among CoNLL shared tasks above). A major drawback of statistical methods is that they require elaborate feature engineering. Since 2015,[22] the statistical approach was replaced by the neural networks approach, using word embeddings to capture semantic properties of words.

This powerful NLP-powered technology makes it easier to monitor and manage your brand’s reputation and get an overall idea of how your customers view you, helping you to improve your products or services over time. Let’s look at an example of NLP in advertising to better illustrate just how powerful it can be for business. There are many eCommerce websites and online retailers that leverage NLP-powered semantic search engines. They aim to understand the shopper’s intent when searching for long-tail keywords (e.g. women’s straight leg denim size 4) and improve product visibility. In the 1950s, Georgetown and IBM presented the first NLP-based translation machine, which had the ability to translate 60 Russian sentences to English automatically.

NLP makes it extremely easy to monitor the performance of the events that you might launch on your social media handles. With NLP, you can also track the total likes, shares, and reach of your posts. SaaS tools are the most accessible way to get started with natural language processing.

“Text analytics is a computational field that draws heavily from the machine learning and statistical modeling niches as well as the linguistics space. In this space, computers are used to analyze text in a way that is similar to a human’s reading comprehension. This opens the door for incredible insights to be unlocked on a scale that was previously inconceivable without massive amounts of manual intervention. The final addition to this list of NLP examples would point to predictive text analysis. You must have used predictive text on your smartphone while typing messages.

Apart from chatbots, intent detection can drive benefits in sales and customer support areas. Its main goal is to simplify the process of going through vast amounts of data, such as scientific papers, news content, or legal documentation. In today’s hyperconnected world, our smartphones have become inseparable companions, constantly gathering and transmitting data about our whereabouts and movements. This trove of information, often referred to as mobile traffic data, holds a wealth of insights about human behaviour within cities, offering a unique perspective on urban dynamics and patterns of movement. NLP can be used to generate these personalized recommendations, by analyzing customer reviews, search history (written or spoken), product descriptions, or even customer service conversations. By analyzing billions of sentences, these chains become surprisingly efficient predictors.

At the demonstration, 60 carefully crafted sentences were translated from Russian into English on the IBM 701. The event was attended by mesmerized journalists and key machine translation researchers. The result of the event was greatly increased funding for machine translation work. Watson is one of the known natural language processing examples for businesses providing companies to explore NLP and the creation of chatbots and others that can facilitate human-computer interaction. The next natural language processing examples for businesses is Digital Genius.