11 NLP Applications & Examples in Business

Now, however, it can translate grammatically complex sentences without any problems. Deep learning is a subfield of machine learning, which helps to decipher the user’s intent, words and sentences. And big data processes will, themselves, continue to benefit from improved NLP capabilities. So many data processes are about translating information from humans (language) to computers (data) for processing, and then translating it from computers (data) to humans (language) for analysis and decision making.

The training data might be on the order of 10 GB or more in size, and it might take a week or more on a high-performance cluster to train the deep neural network. (Researchers find that training even deeper models from even larger datasets have even higher performance, so currently there is a race to train bigger and bigger models from larger and larger datasets). Chatbots are common on so many business websites because they are autonomous and the data they store can be used for improving customer service, managing customer complaints, improving efficiencies, product research and so much more. They can also be used for providing personalized product recommendations, offering discounts, helping with refunds and return procedures, and many other tasks. Chatbots do all this by recognizing the intent of a user’s query and then presenting the most appropriate response.

When it comes to examples of natural language processing, search engines are probably the most common. When a user uses a search engine to perform a specific search, the search engine uses an algorithm to not only search web content based on the keywords provided but also the intent of the searcher. In other words, the search engine “understands” what the user is looking for. For example, if a user searches for “apple pricing” the search will return results based on the current prices of Apple computers and not those of the fruit.

Natural Language Processing, usually shortened as NLP, is a broad branch of artificial intelligence that focuses on the interaction between computers and human languages. Through various techniques, NLP aims at reading, deciphering and making sense of language. As companies and individuals become increasingly globalized, effortless, and smooth communication is a business essential. Currently, more than 100 million people speak 12 different languages worldwide. Even if you hire a skilled translator, there’s a low chance they are able to negotiate deals across multiple countries. In March of 2020, Google unveiled a new feature that allows you to have live conversations using Google Translate.

The process is known as “sentiment analysis” and can easily provide brands and organizations with a broad view of how a target audience responded to an ad, product, news story, etc. For example, sentiment analysis training data consists of sentences together with their sentiment (for example, positive, negative, or neutral sentiment). A machine-learning algorithm reads this dataset and produces a model which takes sentences as input and returns their sentiments. This kind of model, which takes sentences or documents as inputs and returns a label for that input, is called a document classification model. Document classifiers can also be used to classify documents by the topics they mention (for example, as sports, finance, politics, etc.).

Compared to chatbots, smart assistants in their current form are more task- and command-oriented. Too many results of little relevance is almost as unhelpful as no results at all. As a Gartner survey pointed out, workers who are unaware of important information can make the wrong decisions. Even the business sector is realizing the benefits of this technology, with 35% of companies using NLP for email or text classification purposes. Additionally, strong email filtering in the workplace can significantly reduce the risk of someone clicking and opening a malicious email, thereby limiting the exposure of sensitive data.

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. Natural language processing tools help businesses process huge amounts of unstructured data, like customer support tickets, social media posts, survey responses, and more. Current approaches to natural language processing are based on deep learning, a type of AI that examines and uses patterns in data to improve a program’s understanding. The voracious data and compute requirements of Deep Neural Networks would seem to severely limit their usefulness.

Natural language processing plays a vital part in technology and the way humans interact with it. Though it has its challenges, NLP is expected to become more accurate with more sophisticated models, more accessible and more relevant in numerous industries. NLP will continue to be an important part of both industry and everyday life. NLP has existed for more than 50 years and has roots in the field of linguistics. It has a variety of real-world applications in numerous fields, including medical research, search engines and business intelligence.

NLP applies both to written text and speech, and can be applied to all human languages. Other examples of tools powered by NLP include web search, email spam filtering, automatic translation of text or speech, document summarization, sentiment analysis, and grammar/spell checking. For example, some email programs can automatically suggest an appropriate reply to a message based on its content—these programs use NLP to read, analyze, and respond to your message. Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval. It is primarily concerned with giving computers the ability to support and manipulate human language.

Siri, Alexa, or Google Assistant?

One common NLP technique is lexical analysis — the process of identifying and analyzing the structure of words and phrases. In computer sciences, it is better known as parsing or tokenization, and used to convert an array of log data into a uniform structure. If a user opens an online business chat to troubleshoot or ask a question, a computer responds in a manner that mimics a human. Sometimes the user doesn’t even know he or she is chatting with an algorithm.

Social media monitoring uses NLP to filter the overwhelming number of comments and queries that companies might receive under a given post, or even across all social channels. These monitoring tools leverage the previously discussed sentiment analysis and spot emotions like irritation, frustration, happiness, or satisfaction. Let’s look at an example of NLP in advertising to better illustrate just how powerful it can be for business. If https://chat.openai.com/ a marketing team leveraged findings from their sentiment analysis to create more user-centered campaigns, they could filter positive customer opinions to know which advantages are worth focussing on in any upcoming ad campaigns. 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.

We also have Gmail’s Smart Compose which finishes your sentences for you as you type. However, large amounts of information are often impossible to analyze manually. 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. Research on NLP began shortly after the invention of digital computers in the 1950s, and NLP draws on both linguistics and AI.

For example, over time predictive text will learn your personal jargon and customize itself. This is the most commonly used model that allows for the counting of all words in a piece of text. These word frequencies, or occurrences, are then used as features for training a classifier just like in the example of our car pricing. This overly simplistic approach can lead to satisfactory results in some cases, but it has some drawbacks. For example, it does not preserve word order, and the encoded numbers do not convey the meaning of the words. It’s important to assess your options based on your employee and financial resources when making the Build vs. Buy Decision for a Natural Language Processing tool.

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. Data cleaning techniques are essential to getting accurate results when you analyze data for various purposes, such as customer experience insights, brand monitoring, market research, or measuring employee satisfaction.

They use Natural Language Processing to make sense of these words and how they are interconnected to form different sentences. You can also perform sentiment analysis periodically, and understand what customers like and dislike about specific aspects of your business ‒ maybe they love your new feature, but are disappointed about your customer service. Those insights can help you make smarter decisions, as they show you exactly what things to improve. You can foun additiona information about ai customer service and artificial intelligence and NLP. Another one of the crucial NLP examples for businesses is the ability to automate critical customer care processes and eliminate many manual tasks that save customer support agents’ time and allow them to focus on more pressing issues.

It does this by analyzing previous fraudulent claims to detect similar claims and flag them as possibly being fraudulent. This not only helps insurers eliminate fraudulent claims but also keeps insurance premiums low. 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. Watch IBM Data & AI GM, Rob Thomas as he hosts NLP experts and clients, showcasing how NLP technologies are optimizing businesses across industries. Use this model selection framework to choose the most appropriate model while balancing your performance requirements with cost, risks and deployment needs.

Discover Natural Language Processing Tools

The understanding by computers of the structure and meaning of all human languages, allowing developers and users to interact with computers using natural sentences and communication. Natural language understanding (NLU) and natural language generation (NLG) refer to using computers to understand and produce human language, respectively. This is also called “language out” by summarizing by meaningful information into text using a concept known as “grammar of graphics.” Chatbots are a form of artificial intelligence that are programmed to interact with humans in such a way that they sound like humans themselves.

• 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.
• Today, we can’t hear the word “chatbot” and not think of the latest generation of chatbots powered by large language models, such as ChatGPT, Bard, Bing and Ernie, to name a few.
• Then, the user has the option to correct the word automatically, or manually through spell check.
• IBM’s Global Adoption Index cited that almost half of businesses surveyed globally are using some kind of application powered by NLP.

It’s able to do this through its ability to classify text and add tags or categories to the text based on its content. In this way, organizations can see what aspects of their brand or products are most important to their customers and understand sentiment about their products. NLP can be used to great effect in a variety of business operations and processes to make them which of the following is an example of natural language processing? more efficient. One of the best ways to understand NLP is by looking at examples of natural language processing in practice. None of this would be possible without NLP which allows chatbots to listen to what customers are telling them and provide an appropriate response. This response is further enhanced when sentiment analysis and intent classification tools are used.

Sentiment Analysis

Its main goal is to simplify the process of going through vast amounts of data, such as scientific papers, news content, or legal documentation. OCR helps speed up repetitive tasks, like processing handwritten documents at scale. Legal documents, invoices, and letters are often best stored in the cloud, but not easily organized due to the handwritten element. Tools like Microsoft OneNote, PhotoScan, and Capture2Text facilitate the process using OCR software to convert images to text.

Finally, abstract notions such as sarcasm are hard to grasp, even for native speakers. This is why it is important to constantly update our language engine with new content and to continuously train our AI models to decipher intent and meaning quickly and efficiently. SaaS tools are the most accessible way to get started with natural language processing.

Big data and the integration of big data with machine learning allow developers to create and train a chatbot. Another kind of model is used to recognize and classify entities in documents. For each word in a document, the model predicts whether that word is part of an entity mention, and if so, what kind of entity is involved. 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.

We are very satisfied with the accuracy of Repustate’s Arabic sentiment analysis, as well as their and support which helped us to successfully deliver the requirements of our clients in the government and private sector. Accelerate the business value of artificial intelligence with a powerful and flexible portfolio of libraries, services and applications. If you’re interested in learning more about how NLP and other AI disciplines support businesses, take a look at our dedicated use cases resource page.

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. 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 help businesses in customer experience analysis based on certain predefined topics or categories.

MonkeyLearn can help you build your own natural language processing models that use techniques like keyword extraction and sentiment analysis. Ties with cognitive linguistics are part of the historical heritage of NLP, but they have been less frequently addressed since the statistical turn during the 1990s. Because of their complexity, generally it takes a lot of data to train a deep neural network, and processing it takes a lot of compute power and time. Modern deep neural network NLP models are trained from a diverse array of sources, such as all of Wikipedia and data scraped from the web.

NLP business applications come in different forms and are so common these days. For example, spell checkers, online search, translators, voice assistants, spam filters, and autocorrect are all NLP applications. Many companies have more data than they know what to do with, making it challenging to obtain meaningful insights.

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). Finally, looking for customer intent in customer support tickets or social media posts can warn you of customers at risk of churn, allowing you to take action with a strategy to win them back. 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.

Autocomplete and predictive text predict what you might say based on what you’ve typed, finish your words, and even suggest more relevant ones, similar to search engine results. These are the most popular applications of Natural Language Processing and chances are you may have never heard of them! NLP is used in many other areas such as social media monitoring, translation tools, smart home devices, survey analytics, etc.

A great NLP Suite will help you analyze the vast amount of text and interaction data currently untouched within your database and leverage it to improve outcomes, optimize costs, and deliver a better product and customer experience. Corporations are always trying to automate repetitive tasks and focus on the service tickets that are more complicated. They can help filter, tag, and even answer FAQ’s (frequently asked questions) so your employees can focus on the more important service inquiries. Then, the entities are categorized according to predefined classifications so this important information can quickly and easily be found in documents of all sizes and formats, including files, spreadsheets, web pages and social text. The use of NLP in the insurance industry allows companies to leverage text analytics and NLP for informed decision-making for critical claims and risk management processes.

In order to facilitate that process, NLP relies on a handful of transformations that reduce the complexity of the language. For all these reasons, our language represents the exact opposite of what mathematical models are good at. That is, they need clear, unambiguous rules to perform the same tasks over and over.

Depending on the complexity of the chatbots, they can either just respond to specific keywords or they can even hold full conversations that make it tough to distinguish them from humans. First, they identify the meaning of the question asked and collect all the data from the user that may be required to answer the question. Well, it allows computers to understand human language and then analyze huge amounts of language-based data in an unbiased way.

Virtual Assistants, Voice Assistants, or Smart Speakers

To better understand the applications of this technology for businesses, let’s look at an NLP example. 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. Thanks to NLP, you can analyse your survey responses accurately and effectively without needing to invest human resources in this process. It might feel like your thought is being finished before you get the chance to finish typing. Urgency detection helps you improve response times and efficiency, leading to a positive impact on customer satisfaction.

For many businesses, the chatbot is a primary communication channel on the company website or app. It’s a way to provide always-on customer support, especially for frequently asked questions. 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. As natural language processing is making significant strides in new fields, it’s becoming more important for developers to learn how it works.

Top 8 Data Analysis Companies

As a result, it can produce articles, poetry, news reports, and other stories convincingly enough to seem like a human writer created them. NLP uses either rule-based or machine learning approaches to understand the structure and meaning of text. It plays a role in chatbots, voice assistants, text-based scanning programs, translation applications and enterprise software that aids in business operations, increases productivity and simplifies different processes. NLP (Natural Language Processing) is an artificial intelligence technique that lets machines process and understand language like humans do using computational linguistics combined with machine learning, deep learning and statistical modeling. Another one of the common NLP examples is voice assistants like Siri and Cortana that are becoming increasingly popular.

However, transfer learning enables a trained deep neural network to be further trained to achieve a new task with much less training data and compute effort. It consists simply of first training the model on a large generic dataset (for example, Wikipedia) and then further training (“fine-tuning”) the model on a much smaller task-specific dataset that is labeled with the actual target task. Perhaps surprisingly, the fine-tuning datasets can be extremely small, maybe containing only hundreds or even tens of training examples, and fine-tuning training only requires minutes on a single CPU.

NLP is special in that it has the capability to make sense of these reams of unstructured information. Tools like keyword extractors, sentiment analysis, and intent classifiers, to name a few, are particularly useful. These smart assistants, such as Siri or Alexa, use voice recognition to understand our everyday queries, they then use natural language generation (a subfield of NLP) to answer these queries.

Syntax and semantic analysis are two main techniques used in natural language processing. It also includes libraries for implementing capabilities such as semantic reasoning, the ability to reach logical conclusions based on facts extracted from text. Using NLP, more specifically sentiment analysis tools like MonkeyLearn, to keep an eye on how customers are feeling. You can then be notified of any issues they are facing and deal with them as quickly they crop up. Search engines no longer just use keywords to help users reach their search results.

Much of the information created online and stored in databases is natural human language, and until recently, businesses couldn’t effectively analyze this data. Today most people have interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. Chat PG But NLP also plays a growing role in enterprise solutions that help streamline and automate business operations, increase employee productivity, and simplify mission-critical business processes. NLP uses various analyses (lexical, syntactic, semantic, and pragmatic) to make it possible for computers to read, hear, and analyze language-based data.

Natural language processing is developing at a rapid pace and its applications are evolving every day. That’s great news for businesses since NLP can have a dramatic effect on how you run your day-to-day operations. It can speed up your processes, reduce monotonous tasks for your employees, and even improve relationships with your customers. 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.

Learn how these insights helped them increase productivity, customer loyalty, and sales revenue. Infuse powerful natural language AI into commercial applications with a containerized library designed to empower IBM partners with greater flexibility. However, as you are most likely to be dealing with humans your technology needs to be speaking the same language as them. 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.

Expert.ai’s NLP platform gives publishers and content producers the power to automate important categorization and metadata information through the use of tagging, creating a more engaging and personalized experience for readers. 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 main benefit of NLP is that it improves the way humans and computers communicate with each other. The most direct way to manipulate a computer is through code — the computer’s language. Enabling computers to understand human language makes interacting with computers much more intuitive for humans.

We tried many vendors whose speed and accuracy were not as good as

Repustate’s. Arabic text data is not easy to mine for insight, but

with

Repustate we have found a technology partner who is a true expert in

the

field. One of the best NLP examples is found in the insurance industry where NLP is used for fraud detection.

If you’re not adopting NLP technology, you’re probably missing out on ways to automize or gain business insights. Natural Language Processing (NLP) is at work all around us, making our lives easier at every turn, yet we don’t often think about it. From predictive text to data analysis, NLP’s applications in our everyday lives are far-ranging.

The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. Current approaches to NLP are based on machine learning — i.e. examining patterns in natural language data, and using these patterns to improve a computer program’s language comprehension. Chatbots, smartphone personal assistants, search engines, banking applications, translation software, and many other business applications use natural language processing techniques to parse and understand human speech and written text.