Natural Language Processing (NLP), a fascinating branch of artificial intelligence, is fundamentally about the interaction between humans and machines. It focuses on programming computers to process and analyze large amounts of natural language data, which means enabling our machines to understand and interpret human language. NLP is revolutionizing the way we communicate with our devices, making it more intuitive and tailored to the individual. It's a rapidly evolving field, with potential applications that reach far beyond our current understanding.

Here, let's delve into six intriguing examples of how Natural Language Processing (NLP) is currently employed, showcasing the breadth of its applications in our day-to-day interactions.

Chatbots & Virtual Assistants

Chatbots and virtual assistants are used for automatic question answering, designed to understand natural language and deliver an appropriate response through natural language generation Standard question-answering systems follow pre-defined rules, while AI-powered chatbots and virtual assistants can learn from every interaction and understand how they should respond. The best part: they learn from interactions and improve over time.

These intelligent machines are increasingly present at the frontline of customer support, as they can help teams solve up to 80% of all routine queries and route more complex issues to human agents. Available 24/7, chatbots and virtual assistants can speed up response times, and relieve agents from repetitive and time-consuming queries.

NLP in Chatbots & Virtual Assistants

What is it?

A Chatbot or a Virtual Assistant is an artificial intelligence (AI) program that simulates interactive human conversation by using key pre-calculated user phrases and auditory or text-based signals. They're used in dialog systems for various practical purposes including customer service, request routing, or information gathering.

How to use

You can interact with these AI programs through a chat interface or by voice, depending on the bot. For instance, you can ask Siri, Apple's virtual assistant, questions or give commands using your voice. Similarly, you can type out your queries or requests to a chatbot on a website's customer service chat.

Why it is important

While chatbots and virtual assistants are often used interchangeably, they play distinct roles. A chatbot answers questions and assists users with tasks, while a virtual assistant, powered by more advanced AI, learns from interactions to provide more contextual responses. These tools are important because they offer real-time assistance, streamline workflows, and enhance customer service experiences. They can handle multiple inquiries at once, provide instant responses, and operate 24/7, leading to increased customer satisfaction.

For a visual understanding, check out this video explaining the role of chatbots and virtual assistants. Here's an insightful article diving into the differences between AI and chatbots.

Email filters

Email filters are one of the most basic and initial applications of NLP online. It started with spam filters, uncovering certain words or phrases that signal a spam message. But filtering has upgraded, just like early adaptations of NLP. One of the more prevalent, newer applications of NLP is found in Gmail's email classification. The system recognizes if emails belong to one of three categories (primary, social, or promotions) based on their contents. For all Gmail users, this keeps your inbox to a manageable size with important, relevant emails you wish to review and respond to quickly.

NLP in Email Filters

What is it?

Natural Language Processing (NLP) in email filters is a technology that uses machine learning and AI to understand, interpret, and categorize emails based on their content. This advanced approach goes beyond traditional email filtering methods, enabling more nuanced and accurate categorization of emails.

How to use

To utilize NLP in email filters, you need an email client or service that supports this feature. Once enabled, the system automatically processes incoming emails, analyzing their text with NLP algorithms to determine their context, sentiment, urgency, and other aspects. Based on this analysis, emails are then categorized or flagged accordingly.

Why it is important

Understanding NLP's role in email filters is crucial because it represents a significant advancement in email management. Traditional filters rely on basic criteria such as sender address or subject lines, which can be easily manipulated or bypassed. NLP filters, on the other hand, delve into the actual content of the emails, making them more robust and effective at sorting emails, identifying spam, and even flagging potential phishing attempts. This reduces manual effort, improves efficiency, and enhances security.

For more insights into how NLP works with email filters, check out this article that discusses NLP for topic modeling and summarization of documents. There's also a video explaining the basics of NLP and its applications.

Smart assistants

Smart assistants like Apple's Siri and Amazon's Alexa recognize patterns in speech thanks to voice recognition, then infer meaning and provide a useful response. We've become used to the fact that we can say “Hey Siri,” ask a question, and she understands what we said and responds with relevant answers based on context. We're getting used to seeing Siri or Alexa pop up throughout our homes and daily lives as we have conversations with them through items like thermostats, light switches, cars, and more. We now expect assistants like Alexa and Siri to understand contextual clues as they improve our lives and make certain activities easier like ordering items, and even appreciate when they respond humorously or answer questions about themselves. Our interactions will grow more personal as these assistants get to know more about us. As a New York Times article “Why We May Soon Be Living in Alexa's World,” explained: “Something bigger is afoot. Alexa has the best shot of becoming the third great consumer computing platform of this decade.”

NLP in Smart Assistants

What is it?

Natural Language Processing (NLP) in smart assistants is a technology that allows these assistants to understand, process, and respond to human language in a meaningful way. It's the backbone of AI-powered assistants like Siri, Google Assistant, and Alexa, enabling them to interpret user requests, carry out commands, and even engage in conversation.

How to use

To use NLP in smart assistants, you simply interact with your smart assistant as you would with another person. You can ask questions, give commands, or even make requests using natural, conversational language. The NLP technology behind the assistant will interpret your words, carry out the necessary actions, and provide a response in a human-like manner.

Why it is important

Understanding the role of NLP in smart assistants helps to appreciate the complexity and potential of these devices. NLP not only allows smart assistants to understand commands but also to learn from interactions and provide more personalized responses over time. This technology is revolutionizing the way we interact with our devices, making technology more accessible and intuitive, and paving the way for more advanced AI applications in the future.

For a deeper understanding of how NLP works with smart assistants, check out this video on the role of NLP in AI. Here's an insightful article explaining how voice assistants understand us through NLP.

Search results

Search engines use NLP to surface relevant results based on similar search behaviors or user intent so the average person finds what they need without being a search-term wizard. For example, Google not only predicts what popular searches may apply to your query as you start typing, but it also looks at the whole picture and recognizes what you're trying to say rather than the exact search words. Someone could put a flight number in Google and get the flight status, type a ticker symbol and receive stock information, or a calculator might come up when inputting a math equation. These are some variations you may see when completing a search as NLP in search associates the ambiguous query to a relative entity and provides useful results.

NLP in Search Results

What is it?

Natural Language Processing (NLP) in search results refers to the use of AI to understand and interpret human language in search queries. This technology allows search engines like Google to deliver more relevant and precise results by understanding the context and intent behind the words users type into the search bar.

How to use

To utilize NLP in search results, you simply use your search engine as you normally would. The search engine uses NLP to analyze your query, understand the context and intent behind it, and then deliver results that are closely aligned with what you're looking for. For instance, if you type a question into Google, the search engine uses NLP to understand that you're looking for an answer and provides a direct response whenever possible.

Why it is important

Understanding the role of NLP in search results helps to appreciate how sophisticated modern search engines have become. NLP allows these engines to go beyond literal keyword matching, delivering more accurate results even when queries are complex or ambiguous. This not only makes searching more efficient for users but also allows businesses to optimize their online content in a more nuanced way, potentially improving their visibility on the web.

For a deeper understanding of how NLP works with search results, check out this video on Google's BERT algorithm, a major application of NLP in search. Here's an insightful article from Google explaining how they use NLP to improve search results.

Market Intelligence

Marketers can benefit from natural language processing to learn more about their customers and use those insights to create more effective strategies.

Analyzing topics, sentiment, keywords, and intent in unstructured data can boost your market research, shedding light on trends and business opportunities. You can also analyze data to identify customer pain points and to keep an eye on your competitors (by seeing what things are working well for them and which are not).

NLP in Market Intelligence

What is it?

Natural Language Processing (NLP) in market intelligence refers to the use of AI to analyze and interpret human language from various sources like social media posts, customer reviews, or news articles to gain insights about the market. This technology can identify trends, sentiments, and topics that are relevant to a specific industry or product.

How to use

To use NLP in market intelligence, you typically need a tool or platform that supports this feature. These tools will collect data from your chosen sources and then use NLP to analyze the text. The results can be used to inform business decisions, such as product development, marketing strategies, or customer service improvements.

Why it is important

Understanding the role of NLP in market intelligence is crucial because it provides a more nuanced and in-depth understanding of the market. Traditional market research methods can be time-consuming and may miss subtle trends or sentiments that NLP can catch. By analyzing language, NLP can uncover insights about consumer behavior, brand perception, and emerging trends that quantitative data might miss, making it a powerful tool for businesses looking to stay ahead of the competition.

For a deeper understanding of how NLP works with market intelligence, check out this video on the role of NLP in business intelligence. Here's an insightful article explaining how NLP can be used for market intelligence and other applications.

Targeted Advertising

One day I was searching for a mobile phone on Amazon, and a few minutes later, Google started showing me ads related to similar mobile phones on various webpages. I am sure you have experienced it.

Do you know what happened here?

Yeah! You read it right targeted advertising. Targeted advertising is a type of online advertising where ads are shown to the user based on their online activity. Most online companies today use this approach because first, it saves companies a lot of money, and second, relevant ads are shown only to potential customers.

Targeted advertising works mainly on Keyword Matching. The Ads are associated with a keyword or phrase, and it is shown to only those users who search for the keyword similar to the keyword with which the advertisement was associated. That's not enough, there are other factors like the recent websites they visited, and the webpages they showed interest in, which are all taken into account to provide the users with the relevant advertisements of products that they might be interested in. For reading more about the Keyword Matching, click here.

NLP in Targeted Advertising

What is it?

Natural Language Processing (NLP) in targeted advertising is a technology that uses AI to analyze and interpret human language from various data sources like social media, search queries, or customer reviews. This analysis can provide insights into consumer behavior, preferences, and sentiments, which can be used to create more personalized and effective advertising campaigns.

How to use

To use NLP in targeted advertising, you would need a tool or platform that offers this capability. These tools can analyze text data from your chosen sources, identify key themes or sentiments, and then use these insights to inform your advertising strategy. For example, if NLP analysis reveals that customers often mention loving the eco-friendliness of your products, this could be a key selling point to highlight in your ads.

Why it is important

Understanding the role of NLP in targeted advertising can help businesses to create more effective and personalized ad campaigns. By analyzing language, NLP can uncover subtle details about consumer needs and preferences that might not be apparent from quantitative data alone. This allows businesses to tailor their messaging to resonate more strongly with their target audience, potentially leading to higher engagement and conversion rates.

For a deeper understanding of how NLP works with targeted advertising, check out this video on the role of NLP in business intelligence. Here's an insightful article explaining how NLP can be used in the advertising industry.


In conclusion, I encourage you to give one of the tips, techniques, or strategies from the list a try. Whether it's implementing a new approach or experimenting with a different method, taking action and applying these ideas can lead to valuable insights and positive outcomes. So go ahead, and embrace the opportunity for growth and improvement by putting one of these suggestions into practice. Happy exploring!

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