NLP Tasks & Applications
Master core NLP tasks — text classification, sentiment analysis, named entity recognition, question answering, and text generation — with practical implementations.
50 min•By Priygop Team•Last updated: Feb 2026
Core NLP Tasks
- Text Classification: Assigning categories to text — spam/not spam, topic categorization, intent detection. Use BERT fine-tuning or simpler models for small datasets
- Sentiment Analysis: Determining opinion polarity (positive/negative/neutral) or emotion (joy, anger, sadness). Used in brand monitoring, review analysis, social media tracking
- Named Entity Recognition (NER): Extracting entities from text — person names, organizations, locations, dates, monetary amounts. BIO tagging scheme (B-ORG, I-ORG, O)
- Question Answering: Extractive QA (find answer span in context) or Generative QA (generate answer from knowledge). SQuAD benchmark. RAG = Retrieval + Generation
- Machine Translation: Converting text between languages — neural MT (sequence-to-sequence) replaced statistical MT. Google Translate processes 100B+ words/day
- Text Summarization: Extractive (select key sentences) or Abstractive (generate new sentences). Used for news, research papers, meeting notes
- Text Generation: Autoregressive generation word by word — chatbots, creative writing, code generation. Temperature controls randomness
Large Language Models (LLMs)
- What LLMs Are: Massive transformer models trained on internet-scale text (trillions of tokens) that can generate, understand, and reason about text
- Pre-training: Self-supervised learning on massive text corpora — predict next token (GPT) or fill in masked tokens (BERT). Learns grammar, facts, and reasoning
- Fine-tuning: Adapting pre-trained LLM to specific tasks with labeled data. Parameter-efficient methods: LoRA (Low-Rank Adaptation) and QLoRA use 1% of parameters
- Prompt Engineering: Crafting input prompts to get desired outputs without fine-tuning — zero-shot, few-shot, chain-of-thought prompting
- RAG (Retrieval Augmented Generation): Combine LLM with a knowledge base — retrieve relevant documents, then generate answers grounded in retrieved context
- Hallucination Problem: LLMs can generate plausible but false information — mitigation: RAG, fact-checking, confidence estimation, human-in-the-loop
- Deployment: Serve LLMs via APIs (OpenAI, Anthropic) or self-host with vLLM, TGI, or Ollama. Quantization (4-bit, 8-bit) reduces memory requirements