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.

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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.

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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.

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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.

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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.

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5. Machine Translation

• What: Automatically translate text between languages.

• Example:

  “Bonjour tout le monde” → “Hello everyone”

• Why: Core for global communication tools.

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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.

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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.

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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.

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9. Text Classification

• What: Assign predefined labels to text.

• Example:

  “This email is spam.” → Spam

• Why: Email filtering, topic detection.

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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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