Ever asked Siri a question or used Google translate? These cool tools rely on something called Natural Language Processing, or NLP. NLP allows computers to comprehend and manipulate human language. And guess what? Even Class 10 students can learn the basics of NLP! So, this is becoming really quite critical across many domains.
What is natural language processing (NLP)?
NLP stands for ANatural Language Processing, which is to equip computers to understand a human language and use it. It is like teaching a computer to “read” and “write” in a way we know of. It is the interface between human language and computers. NLP has two main parts.
Natural Language Understanding (NLU) in a Nutshell
NLU allows computers to derive meaning from text or speech. It can, for example, determine whether a customer review is cheerful or irate. That’s sentiment analysis! When you ask a question, NLU can also determine what you want. This technique is called intent recognition.
Explaining what is Natural Language Generation (NLG)
NLG is the opposite of NLU. It allows computers to generate text that mimics human writing. This is sort of like a summary of a lengthy news story for you. Or writing a product description for an online store. NLG: Computers Generate Text Similar to Humans
Most Common Concepts in NLP Explained for Novices
There are very few key concepts one must understand to start machine translations in NLP. This would be a solid base for you to work with.
Tokenization: Breaking the Text into Bits
Tokenization — breaking text up into pieces known as tokens. Typically, these tokens also correspond to individual words. This basically means that the sentence “I love NLP” becomes “I” “love” “NLP”. It is better to break sentences into parts.
Elimination of common words (Stop Word Removal)
These are commonly used words such as the, a, and is called stop words. In NLP, these words do not provide a lot of information. Get rid of them so they don’t get in the way of the stuff that matters. If you strip stop words from, “The cat is on the mat,” you get “cat,” “mat.”
More on stemming and lemmatization: how to reduce words to their root form
But stemming and lemmatization is based on finding the root of a word. Stemming cuts off the end of a word. Also, lemmas find the dictionary form. With “running,” for example, stemming could yield “runn.” Lemmatization, though, gives you “run.”
NLP applications in our everyday life
You probably already use NLP every day. You probably don’t think twice about it each day.
==chatbots: Conversational AI assistants==
NLP powers chatbots so they understand what you type and provide helpful answers. Numerous companies utilize them for customer service. Some even use them to teach students! These are conversational AI-based assistants called chatbots.
Oot: [Machine Translation: Bridging Language Barriers]
Natural Language Processing (NLP) is used in machine translation totranslate the languages. A common one is Google Translate. It uses NLP techniques to get the essence of original paragraph. Then it generates a translatioin in a different language. Machine translation frees you from the shackles of language.
Detecting emotions in text: Sentiment Analysis
NLP is used in sentiment analysis to understand how people feel towards something. It could be either positive or negative, depending on the semantics attached to the words. How so, you might ask? Sentiment analysis! It’s also how companies get to know the general opinion of their products. Sentiment analysis understands emotions within text.
Introduction to NLP: Easy Projects Classes For 10 Students
Want to try NLP yourself? Some basic projects to begin with are
How to Create a Generic Sentiment Classifier
Using online tools, you can create your basic sentiment analyzer. A few Python libraries also functions. These tools may help analyze text. You get to see if it has positive, negative, or neutral sentiment.
How to build a Text Summarization Tool?
You could also create a tool for summarising text. It applies NLP and surfaces the most salient sentences in the feed. Then it pairs them up to generate a compressed version of the original content. There are numerous online resources to walk you through the process.
The State of NLP: Threats and Opportunities
NLP is always changing. It has a lot to offer as well as a lot of challenges.
Ethical Considerations in NLP
NLP can sometimes be biased. That means it may not function impartially for all. It can also raise privacy concerns. For example, language models are biased. These are topics you should consider as NLP advances.
Increasing Demand for NLP Skills
More and more companies require humans with NLP skills. Plenty of new job openings are available. If yes, NLP has a great career for you then! Skills in NLP are in rising demand.
In Summary: Learning The Language Of Tomorrow
NLP is a phenomenal technology that is revolutionizing our communication with computers. It’s used in chatbots, translation apps and a whole lot more. You can understand the fundamentals and begin your journey in this amazing domain even at the Class 10 level. So how about building your own sentiment analyzer? • Jump and into the language of tomorrow.