## TL;DR

NLP enables computers to understand, interpret, and generate human language. Tasks: text classification, named entity recognition (NER), machine translation, summarization, question answering, sentiment analysis. Before Transformers (2017): rule-based → statistical → neural (RNN/LSTM). After Transformers: pre-trained language models dominate.

## Core Explanation

Pre-Transformer era: TF-IDF, Word2Vec (Mikolov 2013), GloVe (Pennington 2014) for word embeddings. RNN/LSTM with attention (Bahdanau 2014) for translation. Post-Transformer: BERT (2018), GPT series, T5 — pre-train on massive text, fine-tune for specific task. Current SOTA: large language models (GPT-5, Claude, Gemini) achieve few-shot and zero-shot performance on many NLP tasks.

## Further Reading

- [Speech and Language Processing (3rd Ed, Jurafsky & Martin)](https://web.stanford.edu/~jurafsky/slp3/)