Top 10 Natural Language Processing NLP Applications

He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School. NLP can assist in credit scoring by extracting relevant data from unstructured documents such as loan documentations, income, investments, expenses, etc. and feed it to credit scoring software to determine the credit score. Chatbots have numerous applications in different industries as they facilitate conversations with customers and automate various rule-based tasks, such as answering FAQs or making hotel reservations. Similarly, a multinational corporation may use NLP to translate product descriptions and marketing materials from their original language to the languages of their target markets.

Chatbots help the companies in achieving the goal of smooth customer experience. So, let’s start with the first application of natural language processing. More than a mere tool of convenience, it’s driving serious technological breakthroughs. In one case, Akkio was used to classify the sentiment of tweets about a brand’s products, driving real-time customer feedback and allowing companies to adjust their marketing strategies accordingly. If a negative sentiment is detected, companies can quickly address customer needs before the situation escalates.

Siri, Alexa, or Google Assistant?

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. 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.

NLP is used for a wide variety of language-related tasks, including answering questions, classifying text in a variety of ways, and conversing with users. NLP is growing increasingly sophisticated, yet much work remains to be done. Current systems are prone to bias and incoherence, and occasionally behave erratically. Despite the challenges, machine learning engineers have many opportunities to apply NLP in ways that are ever more central to a functioning society.

Machine Translation

It allows computers to understand the meaning of words and phrases, as well as the context in which they’re used. 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. By enabling computers to understand human language, interacting with computers becomes much more intuitive for humans.

Another reason for the placement of the chocolates can be that people have to wait at the billing counter, thus, they are somewhat forced to look at candies and be lured into buying them. It is thus important for stores to analyze the products their customers purchased/customers’ baskets to know how they can generate more profit. This is an exciting NLP project that you can add to your NLP Projects portfolio for you would have observed its applications almost every day. Well, it’s simple, when you’re typing messages on a chatting application like WhatsApp. We all find those suggestions that allow us to complete our sentences effortlessly. Turns out, it isn’t that difficult to make your own Sentence Autocomplete application using NLP.

The Wonderboard mentioned earlier offers automatic insights by using natural language processing techniques. It simply composes sentences by simulating human speeches by being unbiased. There are calls that are recorded for training purposes but in actuality, they are recorded to the database for an NLP system to learn and improve services in the future. This is also one of the natural language processing examples that are being used by organizations from the last many years.

• Most of the companies use Application Tracking Systems for screening the resumes efficiently.
• The chatbot uses NLP to understand what the person is typing and respond appropriately.
• Smart assistants such as Google’s Alexa use voice recognition to understand everyday phrases and inquiries.
• In 2019, there were 3.4 billion active social media users in the world.
• Before knowing them in detail, let us first understand a few things about NLP.
• 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.

As a result, they can ‘understand’ the full meaning – including the speaker’s or writer’s intention and feelings. MonkeyLearn can help you build your own natural language processing models that use techniques like keyword extraction and sentiment analysis. The voracious data and compute requirements of Deep Neural Networks would seem to severely limit their usefulness. 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.

This information can be used to accurately predict what products a customer might be interested in or what items are best suited for them based on their individual preferences. These recommendations can then be presented to the customer in the form of personalized email campaigns, product pages, or other forms of communication. 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. Words that appear more frequently in the sentence will have a higher numerical value than those that appear less often, and words like “the” or “a” that do not indicate sentiment are ignored.

With it, comes the natural language processing examples leading organizations to bring better results and effective communication with the customers. What comes naturally to humans is challenging for computers in terms of unstructured data, absence of real-word intent, or maybe lack of formal rules. 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).

What is Natural Language Processing (NLP)

Duplicate detection collates content re-published on multiple sites to display a variety of search results. Auto-correct finds the right search keywords if you misspelled something, or used a less common name. Today, NLP has invaded nearly every consumer-facing product from fashion advice bots (like the Stitch Fix bot) to AI-powered landing page bots.

This feature does not merely analyse or identify patterns in a collection of free text but can also deliver insights about a product or service performance that mimics human speech. In other words, let us say someone has a question like “what is the most significant drawback of using freeware? In this case, the software will deliver an appropriate response based on data about how others have replied to a similar question. A smart-search feature offers the same autocomplete services as well as adding relevant synonyms in context to a catalogue to improve search results.

This is repeated until a specific rule is found which describes the structure of the sentence. Natural Language Processing (NLP) refers to AI method of communicating with an intelligent systems using a natural language such as English. Natural Language Processing allows your device to hear what you say, then understand the hidden meaning in your sentence, and finally act on that meaning. But the question this brings is What exactly is Natural Language Processing?

Case Grammar was developed by Linguist Charles J. Fillmore in the year 1968. Case Grammar uses languages such as English to express the relationship between nouns and verbs by using the preposition. In 1957, Chomsky also introduced the idea of Generative Grammar, which is rule based descriptions of syntactic structures. It is an effective and extremely convenient method to search or discover precise information. Spell Check is used by everyone and creates an immense impact on our lives. Everyone has used the spell check feature on a smartphone/laptop/computer.

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. Organizations and potential customers can then interact through the most convenient language and format. NLP is becoming increasingly essential to businesses looking to gain insights into customer behavior and preferences. And companies can use sentiment analysis to understand how a particular type of user feels about a particular topic, product, etc. They can use natural language processing, computational linguistics, text analysis, etc. to understand the general sentiment of the users for their products and services and find out if the sentiment is good, bad, or neutral.

But first and foremost, semantic search is about recognizing the meaning of search queries and content based on the entities that occur. “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. 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. Machines need human input to help understand when a customer is satisfied or upset, and when they might need immediate help.

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