Part 8/15:
The journey of NLP in AI saw numerous stages:
Rule-based systems: Early efforts used hand-coded rules, matching grammar, and dictionaries.
Statistical models and embeddings: In 2003, Mikolov et al. introduced word embeddings, where words like "king" and "queen" could be manipulated via vector arithmetic, capturing semantic and syntactic relationships.
Recurrent neural networks (RNNs): Designed to handle sequences, RNNs and their variants (LSTMs, GRUs) gained popularity but struggled with long-range dependencies.
Attention mechanisms: The revolutionary paper "Attention Is All You Need" (Vaswani et al., 2017) introduced the Transformer architecture, enabling models to weigh contextual relevance dynamically across sequences, greatly improving performance.