Sequence Member Tagging Structure
(Redirected from sequence labeling model)
A sequence member tagging structure is a classification structure that can by applied to a sequence tagging task (to classify each sequence element).
- AKA: String Member Labeling Model.
- It can be used by a Sequence Tagging System (to solve a Sequence Tagging Task (that converts a Sequence to a Tagged Sequence).
- It can be produced by a Supervised Sequence Tagging Task/Sequence Tagging Model Creation Task, such as:
- It can be used as a Chunking Model, using BIO Labels.
- a Finite-State Sequence Tagging Function.
- a Heuristic Sequence Tagging Function.
- a CRF-based Sequence Tagging Function.
- an MEMM-based Sequence Tagging Function.
- an HMM-based Sequence Tagging Function.
- a Maximum-Entropy Function.
- an SVM-based Sequence Tagging Function.
- a Text Tagging Function, such as a POS Tagging Function.
- a DNA Tagging Function.
- a Stochastic Memoizer.
- a Chunking Function.
- a Tree Tagging Function.
- a Lattice Tagging Function.
- a Text Tokenization Function.
- a Sequence Segment Classifier, such as a Named Entity Mention Classifier.
- a Sequence Classifier, such as a Text Classifier.
- a Sentence Parser.
- a Graph Node Classifier.
- See: Data Stream Mining, IOB Tag Set.
- (Ye et al., 2009) ⇒ Nan Ye, Wee Sun Lee, Hai Leong Chieu, and Dan Wu. (2009). “Conditional Random Fields with High-Order Features for Sequence Labeling.” In: Advances in Neural Information Processing Systems 22 (NIPS 2009)
- (Wallach, 2005) ⇒ Hanna M. Wallach. (2005). “Conditional Random Fields." Literature Survey Webpage.
- Conditional random fields (CRFs) are a probabilistic framework for labeling and segmenting structured data, such as sequences, trees and lattices.