Basic NLP Tasks
Basic NLP tasks are the foundational problems in Natural Language Processing — the “building blocks” that most language AI systems solve in some way.
1. Tokenization
What: Splitting text into smaller units like words or subwords.
Example:
Input: “I love NLP.”
Output: ["I", "love", "NLP", "."]
Why: Every NLP model needs text in tokenized form.
2. Part-of-Speech (POS) Tagging
What: Label each word with its grammatical role (noun, verb, adjective…).
Example:
“Dogs run fast.” → [(Dogs, Noun), (run, Verb), (fast, Adverb)]
Why: Useful for syntax-aware systems like grammar checkers.
3. Named Entity Recognition (NER)
What: Find and classify “real-world” entities in text (people, places, organizations).
Example:
“Apple released the iPhone in California.” →
[(Apple, Organization), (iPhone, Product), (California, Location)]
Why: Critical for search engines, chatbots, info extraction.
4. Sentiment Analysis
What: Determine the emotional tone of a text (positive, negative, neutral).
Example:
“This movie was fantastic!” → Positive
Why: Used in social media monitoring, reviews, etc.
5. Machine Translation
What: Automatically translate text between languages.
Example:
“Bonjour tout le monde” → “Hello everyone”
Why: Core for global communication tools.
6. Question Answering (QA)
What: Find answers to questions from text.
Example:
Text: “Paris is the capital of France.”
Q: “What is the capital of France?” → “Paris”
Why: Foundation for search and assistants.
7. Text Summarization
What: Generate a concise version of a longer text.
Example:
Input: A 5-page news article
Output: “Key events summarized in 3 sentences”
Why: Used in news, legal, and research tools.
8. Text Generation
What: Generate new text based on a prompt.
Example:
Prompt: “Write a poem about the sea” →
“The waves crash softly on the shore…”
Why: Core for chatbots, code assistants, content creation.
9. Text Classification
What: Assign predefined labels to text.
Example:
“This email is spam.” → Spam
Why: Email filtering, topic detection.
10. Dependency Parsing
What: Analyze grammatical structure by showing how words relate.
Example:
“She eats an apple.” → “eats” is the root, “She” is subject, “apple” is object.
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