Natural Language Processing and it’s Practical Applications
Though my spouse is not a deaf person but he can’t listen to when I ask for something. Even If he listens he gives confusing replies. It’s not like Alexa, who always pays attention and responds nicely. Maybe I should sneak in some Alexa lessons for him, teach him the art of attentive listening and throwing in a sprinkle of charm when he replies. Time to upgrade his software, I suppose! But unfortunately not available. ;) ;)
Alexa has become my best friend. Last night Alexa reminded me to start preparing dinner at 7 pm and then I asked Alexa to play a song while I was cooking.
This is a blessing of NLP.
NLP stands for “Natural Language Processing.” It’s like teaching computers to understand and talk with people in a way that feels natural, almost like how we talk with our friends.
Imagine if you could ask machine a question and it answer you just like a friend response! NLP helps computers read, understand, and respond to human language, making it easier for us to communicate with them. It’s basically turning our tech into the James Bond of conversation — smooth, suave, and surprisingly good with puns!
Areas of applications:
Though it is unlimited and evolving but some of the real world applications are:
- Contextual Advertisement: When you’re scrolling through your social media or websites, those ads that seem to know exactly what you’re interested in are the result of contextual advertising.
- Emails: When you’re checking your emails, your inbox is like a superhero protecting you from the villains of spam. It blocks those annoying spam emails. On the other hand, automated replies swoop in to save time.
- Understanding Search Queries: Google’s search algorithm uses NLP to understand and interpret user queries. It considers the context, intent, and semantics of the search terms to provide more accurate and relevant results.
- Auto-Completion and Spell Correction: Assists in predicting and auto-completing search queries. It also aids in spell correction, understanding potential errors in the user’s input and suggesting corrections.
- Sentiment Analysis-This process allows for a quick overview, presenting an approximate percentage breakdown of positive, negative, and neutral sentiments, aiding in understanding public perception.
- Google Translate-Translate text from one language to another. Language can’t be a barrier anymore.
- Conversational agents: Chatbots or virtual assistants. Heavily used for customer support nowadays.
- Text summarization: It’s like having a personal storyteller who knows how to cut to the chase and keep it simple for you!
- Topic modeling: like the magic sorting hat from Harry Potter. The model reads through the articles and says, “Okay, this group talks a lot about gadgets and apps, so let’s call it ‘Technology.’ This one is all about matches and scores, so it’s ‘Sports.’ Now you have these neat categories, making it easier to understand and navigate through your articles without reading each one.
- Text generation Having a smart writer friend who can create paragraphs or even entire stories for you.
- Spell checking and grammar correction: When you make spelling mistakes, it’s like a watchful friend saying, “Hey, you meant ‘their,’ not ‘there’.
- Text parsing : It breaks down sentences into smaller parts, understanding the roles of each word like a detective dissecting a mystery.
- Speech-to-text, powered by Natural Language Processing (NLP): A speech-to-text system. You speak into your device, saying, “Remind me to buy groceries at 5 PM.” The NLP magic kicks in, understanding your spoken words, and converts it to text: “Remind me to buy groceries at 5 PM.” Now, your spoken reminder is transformed into written text, ready to be saved or used for any hands-free task.
- Knowledge graphs: Provide a structured framework, organizing entities and relationships within a domain, while Question-Answering (QA) systems leverage NLP to interpret user queries. The synergy between these technologies is evident as QA systems navigate knowledge graphs, extracting relevant information for precise responses. This collaborative approach enhances the efficiency of information retrieval.
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