Definition
Sequence labeling involves predicting a categorical label for every token in a given input sequence, such as words in a sentence or characters in a string. Common applications include Part-of-Speech tagging, Named Entity Recognition (NER), and chunking. The model must capture dependencies between adjacent tokens to ensure consistent labeling, often utilizing architectures like Hidden Markov Models, Conditional Random Fields (CRFs), or Bi-directional LSTMs/Transformers that process context from both directions.
Summary
A natural language processing task where a label is assigned to each element in a sequence of inputs.
Key Concepts
- Token classification
- Contextual dependency
- Bi-directional encoding
- CRF layers
Use Cases
- Named Entity Recognition
- Part-of-Speech Tagging
- Syntactic Chunking