Difference between revisions of "Part of Speech Tagging"
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Some common approaches to POS Tagging include the following: | Some common approaches to POS Tagging include the following: | ||
| − | * Hidden Markov Models based approaches | + | * '''Hidden Markov Models''' based approaches, sometimes referred to as stochastic algorithms in older literature |
| − | * Dynamic Programming/Viterbi-like algorithms (DeRose & Church) | + | * '''Dynamic Programming/Viterbi-like algorithms''' (DeRose & Church) |
| − | * Unsupervised approaches: Brill Tagger (Transformation-based learning), Constraint Grammar, Forward-Backward | + | * '''Unsupervised approaches''': Brill Tagger (Transformation-based learning), Constraint Grammar, Forward-Backward |
== Example Systems == | == Example Systems == | ||
Revision as of 19:08, 31 October 2010
Summary
Part of Speech Tagging (or POS Tagging for short) is a task in the field of computational linguistics which looks at marking a text corpus with the associated word categories known as parts of speech to words.
Common Approaches
Some common approaches to POS Tagging include the following:
- Hidden Markov Models based approaches, sometimes referred to as stochastic algorithms in older literature
- Dynamic Programming/Viterbi-like algorithms (DeRose & Church)
- Unsupervised approaches: Brill Tagger (Transformation-based learning), Constraint Grammar, Forward-Backward
Example Systems
- FastTag - open source implementation of Brill Tagger
- OpenNLP Tagger - based on maximum entropy
- CRF Tagger - based on conditional random fields
- LingPipe - tool kit that contains models for POS tagging
