NLP Tasks
Basic NLP (Natural Language Processing) tasks are foundational for understanding, processing, and generating human language. Here are some core tasks in NLP:
1. Tokenization
- Splitting text into smaller units such as words, sentences, or subwords.
- Example:
- Input: “Natural Language Processing is fun!”
- Output: [“Natural”, “Language”, “Processing”, “is”, “fun”, “!”]
2. Part-of-Speech (POS) Tagging
- Assigning grammatical tags (e.g., noun, verb, adjective) to words in a sentence.
- Example:
- Input: “I love NLP.”
- Output: [(“I”, “PRP”), (“love”, “VBP”), (“NLP”, “NN”)]
3. Named Entity Recognition (NER)
- Identifying and classifying entities (e.g., people, organizations, locations) in text.
- Example:
- Input: “Barack Obama was born in Hawaii.”
- Output: [(“Barack Obama”, “PERSON”), (“Hawaii”, “LOCATION”)]
4. Sentiment Analysis
- Determining the sentiment or emotion conveyed by text (e.g., positive, negative, neutral).
- Example:
- Input: “I love this movie!”
- Output: “Positive”
5. Lemmatization and Stemming
- Reducing words to their root forms.
- Example:
- Input: “running”, “ran”, “runs”
- Output (Lemmatization): “run”
- Output (Stemming): “run”
6. Stop Word Removal
- Removing common words (e.g., “and”, “is”, “the”) that do not add significant meaning.
- Example:
- Input: “The cat is on the mat.”
- Output: [“cat”, “mat”]
7. Text Classification
- Categorizing text into predefined classes or labels.
- Example:
- Input: “This is a sports article.”
- Output: “Sports”
8. Language Modeling
- Predicting the next word in a sequence or assigning probabilities to sequences of words.
- Example:
- Input: “The cat sat on the ___”
- Output: [“mat” (0.8), “chair” (0.1), “floor” (0.1)]
9. Machine Translation
- Translating text from one language to another.
- Example:
- Input: “Hello, how are you?”
- Output: “Hola, ¿cómo estás?”
10. Text Summarization
- Generating a concise summary from a longer text.
- Example:
- Input: “Natural language processing is a subfield of AI. It involves understanding and generating human language.”
- Output: “NLP is a subfield of AI for understanding and generating language.”
11. Coreference Resolution
- Identifying when different words refer to the same entity.
- Example:
- Input: “Alice said she would come.”
- Output: “Alice” -> “she”
12. Question Answering
- Answering questions based on a given context or knowledge.
- Example:
- Input: Context: “Paris is the capital of France.” Question: “What is the capital of France?”
- Output: “Paris”
13. Word Embeddings
- Representing words as continuous vectors in a high-dimensional space.
- Example:
- Input: “king”, “queen”, “man”, “woman”
- Output: Relationships such as “king - man + woman ≈ queen”
14. Spell Checking and Text Normalization
- Correcting misspellings and standardizing text (e.g., “u r” -> “you are”).
- Example:
- Input: “I luv NLP!”
- Output: “I love NLP!”
15. Text Generation
- Generating coherent text from a model or input prompt.
- Example:
- Input: “Once upon a time,”
- Output: “Once upon a time, there lived a brave knight in a faraway kingdom.”
These tasks serve as building blocks for more complex NLP systems and applications, such as chatbots, virtual assistants, and recommendation systems.