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.
Natural Language Processing, or NLP, has emerged as a prominent solution for programming machines to decrypt and understand natural language. Most of the top NLP examples revolve around ensuring seamless communication between technology and people. What is Natural Language Processing? How can you use NLP in everyday life? The answers to these questions would determine the effectiveness of NLP as a tool for innovation.
Artificial intelligence is no longer a fantasy element in science-fiction novels and movies. The adoption of AI through automation and conversational AI tools such as ChatGPT showcases positive emotion towards AI. Natural language processing is a crucial subdomain of AI, which wants to make machines ‘smart’ with capabilities for understanding natural language. Reviews of NLP examples in real world could help you understand what machines could achieve with an understanding of natural language. Let us take a look at the real-world examples of NLP you can come across in everyday life.
Excited to learn the fundamentals of AI applications in business? Enroll now in AI For Business Course
Importance of Natural Language Processing
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.
NLP works through normalization of user statements by accounting for syntax and grammar, followed by leveraging tokenization for breaking down a statement into distinct components. Finally, the machine analyzes the components and draws the meaning of the statement by using different algorithms.
The working mechanism in most of the NLP examples focuses on visualizing a sentence as a ‘bag-of-words’. NLP ignores the order of appearance of words in a sentence and only looks for the presence or absence of words in a sentence. The ‘bag-of-words’ algorithm involves encoding a sentence into numerical vectors suitable for sentiment analysis. For example, words that appear frequently in a sentence would have higher numerical value.
It is important to note that other complex domains of NLP, such as Natural Language Generation, leverage advanced techniques, such as transformer models, for language processing. ChatGPT is one of the best natural language processing examples with the transformer model architecture. Transformers follow a sequence-to-sequence deep learning architecture that takes user inputs in natural language and generates output in natural language according to its training data.
Excited to learn about the fundamentals of Bard AI, its evolution, common tools, and business use cases? Enroll now in the Google Bard AI Course
Why Should You Learn about Examples of NLP?
Natural language processing is closely related to computer vision. It blends rule-based models for human language or computational linguistics with other models, including deep learning, machine learning, and statistical models. What is the importance of the top NLP examples for you? Why should you learn about examples of NLP? You can find the answers to these questions in the benefits of NLP.
First of all, NLP can help businesses gain insights about customers through a deeper understanding of customer interactions. Natural language processing offers the flexibility for performing large-scale data analytics that could improve the decision-making abilities of businesses. NLP could help businesses with an in-depth understanding of their target markets.
Natural language processing tasks such as speech recognition and sentiment analysis provide a plausible approach for personalizing user experiences. The answers to ‘What is the most popular NLP task?’ could lead to multiple debates. However, it is important to understand that NLP helps businesses in bringing their systems closer to the target audience. Natural language processing ensures that businesses can reduce costs while streamlining different processes.
Most important of all, the personalization aspect of NLP would make it an integral part of our lives. From a broader perspective, natural language processing can work wonders by extracting comprehensive insights from unstructured data in customer interactions. The global NLP market might have a total worth of $43 billion by 2025.
Want to understand the importance of ethics in AI, ethical frameworks, principles, and challenges? Enroll now in the Ethics Of Artificial Intelligence (AI) Course
Everyday Examples of Natural Language Processing
The review of best NLP examples is a necessity for every beginner who has doubts about natural language processing. Anyone learning about NLP for the first time would have questions regarding the practical implementation of NLP in the real world. On paper, the concept of machines interacting semantically with humans is a massive leap forward in the domain of technology.
What can you achieve with the practical implementation of NLP? 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.
The most common communication tool for everyone in the world is electronic mail or email. Did you know that the most common NLP examples in real world include email filters? A working professional sends and receives almost 121 business emails every day. You must have a clear idea about the complexity of managing your inbox.
On top of it, you would have to deal with the issues of spam emails. Interestingly, email service providers can leverage natural language processing to automatically filter out unwanted emails. Training of an NLP model by using a training dataset featuring past emails could help it in detecting spam.
It is important to note that email service providers could use NLP for other tasks beyond spam classification. You can review NLP examples like Gmail to identify smarter ways to leverage natural language processing to your advantage. Gmail utilizes natural language processing for creating smart replies or automatic response generation. It also offers the advantage of automatically marking messages as ‘Starred’ and ‘Important.’ With the power of NLP, Gmail enhances its email filters with suggestions for follow-up on certain emails.
Excited to learn about ChatGPT and other AI use cases? Enroll now in ChatGPT Fundamentals Course
Online business has become a major highlight in the domain of technology. Online retailers have identified the importance of personalized recommendations for empowering their source of revenue. It is important to note that customers don’t always look for items that are in stock or the ones on sale. On the contrary, customers want products that can fulfill their needs while aligning with their values. For example, the recommendation engine of Amazon drives around 35% of the sales on its e-commerce platform.
The outline of natural language processing examples must emphasize the possibility of using NLP for generating personalized recommendations for e-commerce. How can NLP achieve such tasks? NLP models could analyze customer reviews and search history of customers through text and voice data alongside customer service conversations and product descriptions.
The models could subsequently use the information to draw accurate predictions regarding the preferences of customers. Businesses can use product recommendation insights through personalized product pages or email campaigns targeted at specific groups of consumers.
Analysis of Unstructured Data
The most prominent highlight in the debate between NLP and machine learning is the fact that NLP could support unstructured data. Machine learning algorithms must rely on structured datasets. On the contrary, over 80% of available data is in unstructured form, distributed across news articles, emails, books, customer reviews, and user comments.
Interestingly, the response to “What is the most popular NLP task?” could point towards effective use of unstructured data to obtain business insights. Natural language processing could help in converting text into numerical vectors and use them in machine learning models for uncovering hidden insights.
For example, NLP can take thousands of unstructured documents about a business, including earning transcripts, customer reviews, and quarterly reports as inputs. NLP algorithms could use unstructured inputs for identifying and tracking crucial metrics, including sales growth, competitive differentiation, and customer sentiment.
Want to learn about the fundamentals of AI and Fintech, Enroll now in AI And Fintech Masterclass
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. Chatbots were the earliest examples of virtual assistants prepared for solving customer queries and service requests. The first chatbot was created in 1966, thereby validating the extensive history of technological evolution of chatbots.
Natural Language Processing has created the foundations for improving the functionalities of chatbots. One of the popular examples of such chatbots is the Stitch Fix bot, which offers personalized fashion advice according to the style preferences of the user.
The examples of NLP use cases in everyday lives of people also draw the limelight on language translation. Natural language processing algorithms emphasize linguistics, data analysis, and computer science for providing machine translation features in real-world applications. The outline of NLP examples in real world for language translation would include references to the conventional rule-based translation and semantic translation.
You can also find more sophisticated models, like information extraction models, for achieving better results. The models are programmed in languages such as Python or with the help of tools like Google Cloud Natural Language and Microsoft Cognitive Services.
Understand the true potential of generative AI and become an expert with the Generative AI skill path
Customer Sentiment Analysis
How many times have you come across a feedback form online? Tools such as Google Forms have simplified customer feedback surveys. At the same time, NLP could offer a better and more sophisticated approach to using customer feedback surveys. The top NLP examples in the field of consumer research would point to the capabilities of NLP for faster and more accurate analysis of customer feedback to understand customer sentiments for a brand, service, or product.
The most popular example of NLP use cases of customer sentiment analysis is Amazon. Amazon uses automatic sentiment analysis for online reviews. On the other hand, telecom companies such as Verizon utilize NLP for customer sentiment analysis through customer feedback in a call center.
The effective classification of customer sentiments about products and services of a brand could help companies in modifying their marketing strategies. For example, businesses can recognize bad sentiment about their brand and implement countermeasures before the issue spreads out of control.
Smart Virtual Assistants
The different examples of natural language processing in everyday lives of people also include smart virtual assistants. You can notice that smart assistants such as Google Assistant, Siri, and Alexa have gained formidable improvements in popularity. The voice assistants are the best NLP examples, which work through speech-to-text conversion and intent classification for classifying inputs as action or question. Smart virtual assistants could also track and remember important user information, such as daily activities.
Smart virtual assistants are the most complex examples of NLP applications in everyday life. However, the emerging trends for combining speech recognition with natural language understanding could help in creating personalized experiences for users.
Predictive Text Analysis
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. Google is one of the best examples of using NLP in predictive text analysis. Predictive text analysis applications utilize a powerful neural network model for learning from the user behavior to predict the next phrase or word. On top of it, the model could also offer suggestions for correcting the words and also help in learning new words.
Become a master of generative AI applications by developing expert-level skills in prompt engineering with Prompt Engineer Career Path
The review of top NLP examples shows that natural language processing has become an integral part of our lives. 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.
You must also take note of the effectiveness of different techniques used for improving natural language processing. The advancements in natural language processing from rule-based models to the effective use of deep learning, machine learning, and statistical models could shape the future of NLP. Learn more about NLP fundamentals and find out how it can be a major tool for businesses and individual users.