Sequence-Member Tagging Task
(Redirected from sequence labeling)
- AKA: String Labeling, Sequence Item Classification.
- output: a Tagged Sequence.
- It can be solved by a String Tagging System(a Tagger) that implements a String Tagging Algorithm.
- It can range from being a Supervised Sequence-Member Tagging Task to being an Unsupervised Sequence-Member Tagging Task.
- It can be used to solve a Sequence Segmentation Task, if there are few segment types. (Sun et al., 2008)
- any Text Token Classification Task.
- a Data Stream Tagging Task/Data Stream Classification Task.
- See: Data Stream Mining Task, Online Learning Task.
- (Sha & Pereira, 2003a) ⇒ Fei Sha, and Fernando Pereira. (2003). “Shallow Parsing with Conditional Random Fields.” In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology (HLT-NAACL 2003). doi:10.3115/1073445.1073473
- QUOTE: Sequence analysis tasks in language and biology are often described as mappings from input sequences to sequences of labels encoding the analysis. In language processing, examples of such tasks include part-of-speech tagging, named-entity recognition, and the task we shall focus on here, shallow parsing.
- (Collins, 2002a) ⇒ Michael Collins. (2002). “Ranking Algorithms for Named–Entity Extraction: Boosting and the voted perceptron.” In: Proceedings of the ACL Conference (ACL 2002).
- QUOTE: The problem can be framed as a tagging task – to tag each word as being either the start of an entity, a continuation of an entity, or not to be part of an entity at all (we will use the tags S, C and N respectively for these three cases).