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
Personalized Recommendations
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,…
