8 Real-World Examples of Natural Language Processing NLP

NLP also enables computer-generated language close to the voice of a human. Phone calls to schedule appointments like an oil change or haircut can be automated, as evidenced by this video showing Google Assistant making a hair appointment. Through AI, fields like machine learning and deep learning are opening eyes to a world of all possibilities. Machine learning is increasingly being used in data analytics to make sense of big data. It is also used to program chatbots to simulate human conversations with customers. However, these forward applications of machine learning wouldn’t be possible without the improvisation of Natural Language Processing (NLP).

Tokenization can be defined as breaking a sentence into smaller components like meaningful little units, symbols, words, and phrases. Working in NLP can be both challenging and rewarding as it requires a good understanding of both computational and linguistic principles. NLP is a fast-paced and rapidly changing field, so it is important for individuals working in NLP to stay up-to-date with the latest developments and advancements. Meanwhile, as technology has been in the market for the last many years and is being used by us in various ways, there are companies that have accepted the technology wholeheartedly and are now using it fully for business operations. Natural language processing example projects its potential from the last many years and is still evolving for more developed results. Marketing is the most important practice a business commonly works upon to list them among the successful businesses.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Deep 6 AI developed a platform that uses machine learning, NLP and AI to improve clinical trial processes. Healthcare professionals use the platform to sift through structured and unstructured data sets, determining ideal patients through concept mapping and criteria gathered from health backgrounds. Based on the requirements established, teams can add and remove patients to keep their databases up to date and find the best fit for patients and clinical trials. By converting the text into numerical vectors (using techniques like word embeddings) and feeding those vectors into machine learning models, it’s possible to uncover previously hidden insights from these “dark data” sources. In our globalized economy, the ability to quickly and accurately translate text from one language to another has become increasingly important.

Machine Translation

A widespread example of speech recognition is the smartphone’s voice search integration. This feature allows a user to speak directly into the search engine, and it will convert the sound into text, before conducting a search. 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. The saviors for students and professionals alike – autocomplete and autocorrect – are prime NLP application examples. Autocomplete (or sentence completion) integrates NLP with specific Machine learning algorithms to predict what words or sentences will come next, in an effort to complete the meaning of the text. 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.

Accelerate the business value of artificial intelligence with a powerful and flexible portfolio of libraries, services and applications. The Python programing language provides a wide range of tools and libraries for attacking specific NLP tasks. Many of these are found in the Natural Language Toolkit, or NLTK, an open source collection of libraries, programs, and education resources for building NLP programs. The all new enterprise studio that brings together traditional machine learning along with new generative AI capabilities powered by foundation models.

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And this is how natural language processing techniques and algorithms work. And this is not the end, there is a list of natural language processing applications in the market, and more are about to enter the domain for better services. And there are many natural language processing examples that we all are using for the last many years.

Smart virtual assistants could also track and remember important user information, such as daily activities. IBM equips businesses with the Watson Language Translator to quickly translate content into various languages with global audiences in mind. With glossary and phrase rules, companies are able to customize this AI-based tool to fit the market and context they’re targeting. Machine learning and natural language processing technology also enable IBM’s Watson Language Translator to convert spoken sentences into text, making communication that much easier.

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Deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been applied to tasks such as sentiment analysis and machine translation, achieving state-of-the-art results. The next natural language processing classification text analytics converts unstructured text data into structured and meaningful data for further analysis. The data converted for the analysis procedure is taken by using different linguistics, statistical, and machine learning techniques. The review of top NLP examples shows that natural language processing has become an integral part of our lives.

Finally, the machine analyzes the components and draws the meaning of the statement by using different algorithms. The rise of human civilization can be attributed to different aspects, including knowledge and innovation. However, it is also important to emphasize the ways in which people all over the world have been sharing knowledge and new ideas. You will notice that the concept of language plays a crucial role in communication and exchange of information.

Getting started with one process can indeed help us pave the way to structure further processes for more complex ideas with more data. 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. To better understand the applications of this technology for businesses, let’s look at an NLP example. Spellcheck is one of many, and it is so common today that it’s often taken for granted.

This may be accomplished by decreasing usage of superlative or adverbial forms, or irregular verbs. Typical purposes for developing and implementing a controlled natural language are to aid understanding by non-native speakers or to ease computer processing. An example of a widely-used controlled natural language is Simplified Technical English, which was originally developed for aerospace and avionics industry manuals. After performing the preprocessing steps, you then give your resultant data to a machine learning algorithm like Naive Bayes, etc., to create your NLP application. Natural Language Processing or NLP refers to the branch of Artificial Intelligence that gives the machines the ability to read, understand and derive meaning from human languages. Ultimately, it is necessary to eliminate all the ambiguities that natural language has.

For example- developing a deep understanding of the linguistic structure, making search engines, and bots mimic real-life sales agents like roles. One of the most common applications of NLP is in virtual assistants like Siri, Alexa, and Google Assistant. These AI-powered tools understand and process human speech, allowing users to interact with their devices using natural language. This technology has revolutionized how we search for information, control smart home devices, and manage our schedules. By capturing the unique complexity of unstructured language data, AI and natural language understanding technologies empower NLP systems to understand the context, meaning and relationships present in any text.

It defines the ways in which we type inputs on smartphones and also reviews our opinions about products, services, and brands on social media. At the same time, NLP offers a promising tool for bridging communication barriers worldwide by offering language translation functions. Businesses use sentiment analysis to gauge public opinion about their products or services. This NLP application analyzes social media posts, reviews, and comments to understand customer sentiments. By processing large volumes of text data, companies can gain insights into customer satisfaction and market trends, helping them to make data-driven decisions. As mentioned earlier, virtual assistants use natural language generation to give users their desired response.

On average, retailers with a semantic search bar experience a 2% cart abandonment rate, which is significantly lower than the 40% rate found on websites with a non-semantic search bar. SpaCy and Gensim are examples of code-based libraries examples of natural languages that are simplifying the process of drawing insights from raw 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.

The field of NLP has been around for decades, but recent advances in machine learning have enabled it to become increasingly powerful and effective. Companies are now able to analyze vast amounts of customer data and extract insights from it. This can be used for a variety of use-cases, including customer segmentation and marketing personalization. With automatic summarization, NLP algorithms can summarize the most relevant information from content and create a new, shorter version of the original content. It can do this either by extracting the information and then creating a summary or it can use deep learning techniques to extract the information, paraphrase it and produce a unique version of the original content.

A financial news chatbot, for example, that is asked a question like “How is Google doing today? ” will most likely scan online finance sites for Google stock, and may decide to select only information like price and volume as its reply. These are the top 7 solutions for why should businesses use natural language processing and the list is never-ending.

People understand language that flows the way they think, and that follows predictable paths so gets absorbed rapidly and without unnecessary effort. Here at Thematic, we use NLP to help customers identify recurring patterns in their client feedback data. We also score how positively or negatively customers feel, and surface ways to improve their overall experience. Generally, computer-generated content lacks the fluidity, emotion and personality that makes human-generated content interesting and engaging. However, NLG can be used with NLP to produce humanlike text in a way that emulates a human writer. This is done by identifying the main topic of a document and then using NLP to determine the most appropriate way to write the document in the user’s native language.

For example, an application that allows you to scan a paper copy and turns this into a PDF document. After the text is converted, it can be used for other NLP applications like sentiment analysis and language translation. 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.

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. 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. Companies can also use natural language understanding software in marketing campaigns by targeting specific groups of people with different messages based on what they’re already interested in. Using a natural language understanding software will allow you to see patterns in your customer’s behavior and better decide what products to offer them in the future.

Through these examples of natural language processing, you will see how AI-enabled platforms understand data in the same manner as a human, while decoding nuances in language, semantics, and bringing insights to the forefront. Natural Language Processing (NLP) is a concept that has entered our lives as a result of the joint studies conducted in the development of Artificial Intelligence and linguistics. NLP powers many applications that use language, such as text translation, voice recognition, text summarization, and chatbots. You may have used some of these applications yourself, such as voice-operated GPS systems, digital assistants, speech-to-text software, and customer service bots.

What Is Syntactic Analysis In NLP?

In the last decade, a significant change in NLP research has resulted in the widespread use of statistical approaches such as machine learning and data mining on a massive scale. The need for automation is never-ending courtesy of the amount of work required to be done these days. NLP is a very favorable, but aspect when it comes to automated applications. The applications of NLP have led it to be one of the most sought-after methods of implementing machine learning. Natural Language Processing (NLP) is a field that combines computer science, linguistics, and machine learning to study how computers and humans communicate in natural language. The goal of NLP is for computers to be able to interpret and generate human language.

It also includes libraries for implementing capabilities such as semantic reasoning, the ability to reach logical conclusions based on facts extracted from text. Arguably one of the most well known examples of NLP, smart assistants have become increasingly integrated into our lives. Applications like Siri, Alexa and Cortana are designed to respond to commands issued by both voice and text. They can respond to your questions via their connected knowledge bases and some can even execute tasks on connected “smart” devices.

• Translation services like Google Translate use NLP to provide real-time language translation.
• When it comes to large businesses, keeping a track of, facilitating and analyzing thousands of customer interactions for improving services & products.
• With its ability to process human language, NLP is allowing companies to analyze vast amounts of customer data quickly and effectively.

So a document with many occurrences of le and la is likely to be French, for example. Likewise, NLP is useful for the same reasons as when a person interacts with a generative AI chatbot or AI voice assistant. Instead of needing to use specific predefined language, a user could interact with a voice assistant like Siri on their phone using their regular diction, and their voice assistant will still be able to understand them. However, as you are most likely to be dealing with humans your technology needs to be speaking the same language as them. When you send out surveys, be it to customers, employees, or any other group, you need to be able to draw actionable insights from the data you get back. Chatbots might be the first thing you think of (we’ll get to that in more detail soon).

Customer Support

For example, businesses can recognize bad sentiment about their brand and implement countermeasures before the issue spreads out of control. The next entry among popular NLP examples draws attention towards chatbots. As a matter of fact, chatbots had already made their mark before the arrival of smart assistants such as Siri and Alexa.

It is used in many applications such as machine translation, speech recognition, and text classification. With social media listening, businesses can understand what their customers and others are saying about their brand or products on social media. NLP helps social media sentiment analysis to recognize and understand all types of data including text, videos, images, emojis, hashtags, etc.

Many people don’t know much about this fascinating technology and yet use it every day. 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.

Interestingly, the Bible has been translated into more than 6,000 languages and is often the first book published in a new language. By counting the one-, two- and three-letter sequences in a text (unigrams, bigrams and trigrams), a language can be identified from a short sequence of a few sentences only. 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. 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.

There has recently been a lot of hype about transformer models, which are the latest iteration of neural networks. Transformers are able to represent the grammar of natural language in an extremely deep and sophisticated way and have improved performance of document classification, text generation and question answering systems. Natural language capabilities are being integrated into data analysis workflows as more BI vendors offer a natural language interface to data visualizations.

Turns out, these recordings may be used for training purposes, if a customer is aggrieved, but most of the time, they go into the database for an NLP system to learn from and improve in the future. Automated systems direct customer calls to a service representative or online chatbots, which respond to customer requests with helpful information. This is a NLP practice that many companies, including large telecommunications providers have put to use.

Apart from that, NLP helps with identifying phrases and keywords that can denote harm to the general public, and are highly used in public safety management. They also help in areas like child and human trafficking, conspiracy theorists who hamper security details, preventing digital harassment and bullying, and other such areas. Another one of the common NLP examples is voice assistants like Siri and Cortana that are becoming increasingly popular. These assistants use natural language processing to process and analyze language and then use natural language understanding (NLU) to understand the spoken language.

Today, employees and customers alike expect the same ease of finding what they need, when they need it from any search bar, and this includes within the enterprise. Now, thanks to AI and NLP, algorithms can be trained on text in different languages, making it possible to produce the equivalent meaning in another language. This technology even extends to languages like Russian and Chinese, which are traditionally more difficult to translate due to their different alphabet structure and use of characters instead of letters. For example, if you’re on an eCommerce website and search for a specific product description, the semantic search engine will understand your intent and show you other products that you might be looking for. Translation applications available today use NLP and Machine Learning to accurately translate both text and voice formats for most global languages. Natural language processing (NLP) is a branch of Artificial Intelligence or AI, that falls under the umbrella of computer vision.

It’s one of the most widely used NLP applications in the world, with Google alone processing more than 40 billion words per day. Akkio’s no-code AI platform lets you build and deploy a model into a chatbot easily. For instance, Akkio has been used to create a chatbot that automatically predicts credit eligibility for users of a fintech service.

Here is where natural language processing comes in handy — particularly sentiment analysis and feedback analysis tools which scan text for positive, negative, or neutral emotions. Known for offering next-generation customer service solutions, TaskUs, is the next big natural language processing example for businesses. By using it, companies can take advantage of their automation processes for delivering solutions to customers faster. Natural Language Processing, or NLP, is a subdomain of artificial intelligence and focuses primarily on interpretation and generation of natural language. It helps machines or computers understand the meaning of words and phrases in user statements. The most prominent highlight in all the best NLP examples is the fact that machines can understand the context of the statement and emotions of the user.

The NLP practice is focused on giving computers human abilities in relation to language, like the power to understand spoken words and text. For further examples of how natural language processing can be used to your organisation’s efficiency and profitability please don’t hesitate to contact Fast Data Science. Today, Google Translate covers an astonishing array of languages and handles most of them with statistical models trained on enormous corpora of text which may not even be available in the language pair. Transformer models have allowed tech giants to develop translation systems trained solely on monolingual text. Natural language processing plays a vital part in technology and the way humans interact with it.

• Natural Language Understanding (NLU) is the ability of a computer to understand human language.
• Natural language processing (NLP) is a field of computer science and artificial intelligence that aims to make computers understand human language.
• Using Waston Assistant, businesses can create natural language processing applications that can understand customer and employee languages while reverting back to a human-like conversation manner.
• The Georgetown — IBM experiment carried out in 1954 is the first significant breakthrough in the field of NLP research.

Just like any new technology, it is difficult to measure the potential of NLP for good without exploring its uses. Most important of all, you should check how natural language processing comes into play in the everyday lives of people. Here are some of the top examples of using natural language processing in our everyday lives. 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. Several prominent clothing retailers, including Neiman Marcus, Forever 21 and Carhartt, incorporate BloomReach’s flagship product, BloomReach Experience (brX). The suite includes a self-learning search and optimizable browsing functions and landing pages, all of which are driven by natural language processing.

For example- Phone calls for scheduling appointments like haircuts, restaurant timings, etc, can be scheduled with the help of NLP. Autocorrect, autocomplete, predict analysis text is the core part of smartphones that have been unnoticed. Creating a perfect code frame is hard, but thematic analysis software makes the process much easier. Spam detection removes pages that match search keywords but do not provide the actual search answers.