13 Mar NLP – Overview and Tasks

Natural Language Processing (NLP) is a field of artificial intelligence (AI) that focuses on the interaction between computers and humans through natural language. The goal of NLP is to enable computers to understand, interpret, generate, and respond to human language in a way that is both meaningful and useful.

NLP is like teaching a computer to understand human language. Imagine you’re teaching a robot to read, write, and even chat with you. That’s what NLP is all about—making computers smart enough to handle words and sentences.

What is NLP?

NLP is a mix of computer science and linguistics (the study of language). It helps computers:

• Read text (like books, articles, or tweets).
• Understand what the text means.
• Respond in a way that makes sense (like chatbots or voice assistants).

For example, when you ask Siri, “What’s the weather today?” NLP is what helps Siri understand your question and give you the right answer.

Here are some key points:

• It lets computers do things like translate languages (e.g., Google Translate).
• It powers voice assistants like Alexa or Google Assistant.
• It helps spell checkers fix your mistakes.
• It even helps recommendation systems (like Netflix or Spotify) understand what you like based on your reviews or comments.

Key tasks in NLP

1. Text Tokenization: Breaking down text into individual words or phrases.
2. Part-of-Speech Tagging: Identifying the grammatical parts of speech (nouns, verbs, adjectives, etc.) in a text.
3. Named Entity Recognition (NER): Detecting and classifying entities such as names, dates, and locations.
4. Sentiment Analysis: Determining the emotional tone or sentiment behind a piece of text.
5. Machine Translation: Automatically translating text from one language to another.
6. Speech Recognition: Converting spoken language into text.
7. Text Summarization: Condensing a large piece of text into a shorter version while retaining the key information.
8. Question Answering: Automatically generating answers to questions posed in natural language.
9. Text Generation: Creating coherent and contextually relevant text based on given input.

How Does NLP Work

NLP breaks down language into smaller pieces so computers can process it. For example:

1. Tokenization: Splitting a sentence into words (e.g., “I love NLP” → [“I”, “love”, “NLP”]).
2. Understanding Meaning: Figuring out what each word means and how they relate to each other.
3. Responding: Generating a reply or taking action based on what it understood.

Example of NLP

Let’s say you type: “Hey Siri, set a timer for 10 minutes.”

1. Siri uses NLP to understand your words.
2. It figures out you want a timer for 10 minutes.
3. It responds by setting the timer and saying, “10-minute timer set.”

Why is NLP Important

Language is how humans communicate, so teaching computers to understand it opens up a world of possibilities. From helping people with disabilities to making apps smarter, NLP is everywhere!

In short, NLP is like teaching a computer to speak human.

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