Sequence Labeling

Sequence labeling assigns a discrete label to each token in an input sequence. Named entity recognition and part-of-speech tagging are canonical examples. Classical approaches used Hidden Markov Models or Conditional Random Fields that model dependencies between adjacent labels; neural approaches use BiLSTMs or fine-tuned transformers. The BIO tagging scheme — Beginning, Inside, Outside — is a standard encoding that allows multi-token spans to be represented as per-token label sequences.