6 Real-World Examples of Natural Language Processing

• andrewmichaelfriedrichs
• November 9, 2021
• Software development
• 0 Comments

Modern-day technology can automate these processes, taking the task of contextualizing language solely off of human beings. Before diving further into those examples, let’s first examine what natural language processing is and why it’s vital to your commerce business. As mentioned earlier, virtual assistants use natural language generation to give users their desired response. To note, another one of the great examples of natural language processing is GPT-3 which can produce human-like text on almost any topic. The model was trained on a massive dataset and has over 175 billion learning parameters. As a result, it can produce articles, poetry, news reports, and other stories convincingly enough to seem like a human writer created them.

• She researches on issues related to public-private partnerships and innovation at the federal, state, and local government level.
• Within reviews and searches it can indicate a preference for specific kinds of products, allowing you to custom tailor each customer journey to fit the individual user, thus improving their customer experience.
• Similarly, support ticket routing, or making sure the right query gets to the right team, can also be automated.
• The challenge for AI and machine learning has always been figuring out just what those main ideas and keywords are.
• Additionally, that technology has the potential to produce even more sophisticated chatbots and virtual assistants that can comprehend complicated questions, sarcasm, and emotions, dramatically improving the user experience.
• Personalized marketing is one possible use for natural language processing examples.

Like many resellers and business owners alike, if negative reviews are spread on social media, they can ruin a brand’s reputation overnight. Natural language processing is just beginning to demonstrate its true impact on business operations across many industries. Here are just some of the most common applications of NLP in some of the biggest industries around the world. And the guides are not one dimensional; take, for example, the text classification notebooks.

Simple Ways Businesses Can Use Natural Language Processing

Unfortunately, the machine reader sometimes had  trouble deciphering comic from tragic. “The decisions made by these systems can influence user beliefs and preferences, which in turn affect the feedback the learning system receives — thus creating a feedback loop,” researchers for Deep Mind wrote in a 2019 study. 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.

This article will help you understand the basic and advanced NLP concepts and show you how to implement using the most advanced and popular NLP libraries – spaCy, Gensim, Huggingface and NLTK. For years, trying to translate a sentence from one language to another would consistently return confusing and/or offensively incorrect results. This was so prevalent that many questioned if it would ever be possible to accurate translate text. Neha Malik is an Assistant Manager with the Deloitte Center for Government Insights. She researches on issues related to public-private partnerships and innovation at the federal, state, and local government level.

Semantic Analysis

If retailers can make sense of all this data, your product search — and digital experience as a whole — stands to become smarter and more intuitive with language detection and beyond. Overall, this will help your business offer personalized search results, product recommendations, and promotions to drive more revenue. By using this powerful combination of machine learning and natural language processing, your brand can find an edge in a highly competitive and oversaturated market, scale your organization, and cut down on manual processes. NLP algorithms are designed to recognize patterns in human language and extract meaning from text or speech.

That’s why sites like Quora resort to NLP in reducing duplicity in questions as much as possible. After a user ends typing their query on Quora, their NLP mechanics take over and analyze if it bears linguistic similarity to the other questions on the site. Just like autocomplete, NLP technology sets the foundations of autocorrect applications of NLP. Here, NLP identifies the phrase closest https://www.globalcloudteam.com/ to your typo and automatically changes your wrong expression to the correct one. Autocomplete helps Google predict what you’re interested in based on the first few characters or words you enter. NLG pertains to a computer’s ability to create its own communication, whereas NLU is about a system’s ability to understand the jargon, mispronunciations, misspellings, and other language variants.

Extractive Text Summarization using Gensim

Through this enriched social media content processing, businesses are able to know how their customers truly feel and what their opinions are. 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. As well as understanding what people are saying, machines can now understand the emotional context behind those words. Known as sentiment analysis, this can be used to measure customer opinions, monitor a company’s reputation, and generally understand whether customers are happy with a product or service.

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. Applications of text extraction include sifting through incoming support tickets and identifying specific data, like company names, order numbers, and email addresses without needing to open and read every ticket.

Why NLP is the Future?

This requires a deep understanding of the nuances of human communication, including grammar, syntax, context, and cultural references. By analyzing vast amounts of data, NLP algorithms can learn to recognize these patterns and make accurate predictions about language use. AI and NLP are deeply interconnected, with NLP serving as a key component of many AI-powered applications.

Next , you can find the frequency of each token in keywords_list using Counter. The list of keywords is passed as input to the Counter,it returns a dictionary of keywords and their frequencies. The summary obtained from this method will contain the key-sentences of the original text corpus. It can be done through many methods, I will show you using gensim and spacy. Every token of a spacy model, has an attribute token.label_ which stores the category/ label of each entity.

Smart Search

Text classification takes your text dataset then structures it for further analysis. It is often used to mine helpful data from customer reviews as well as customer service slogs. As you can see in the example below, NER is similar to sentiment analysis. NER, however, simply tags the identities, Natural Language Processing Examples in Action whether they are organization names, people, proper nouns, locations, etc., and keeps a running tally of how many times they occur within a dataset. Natural language processing, the deciphering of text and data by machines, has revolutionized data analytics across all industries.

Explore the possibility to hire a dedicated R&D team that helps your company to scale product development. Businesses in the digital economy continuously seek technical innovations to improve operations and give them a competitive advantage. A new wave of innovation in corporate processes is being driven by NLP, which is quickly changing the game. This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals. Laurie is a freelance writer, editor, and content consultant and adjunct professor at Fisher College.

The evolution of NLP

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. Expert.ai’s NLP platform allows publishers and content producers to automate essential categorization and metadata information through tagging, creating readers’ more exciting and personalized experiences. The media can also have content tips so that users can see only the content that is most relevant to them. When suggesting keywords relevant to you, Google relies on a wealth of data that catalogs what other consumers search for when entering specific search terms.