Difference between revisions of "Part of Speech Tagging"
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* Hidden Markov Models based approaches | * Hidden Markov Models based approaches | ||
* Dynamic Programming/Viterbi-like algorithms (DeRose & Church) | * Dynamic Programming/Viterbi-like algorithms (DeRose & Church) | ||
| − | * ''Unsupervised'' algorithms: Brill Tagger, Constraint Grammar, Forward-Backward | + | * ''Unsupervised'' algorithms: Brill Tagger (Transformation-based learning), Constraint Grammar, Forward-Backward |
== Challenges / Issues == | == Challenges / Issues == | ||
Revision as of 18:53, 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
- Dynamic Programming/Viterbi-like algorithms (DeRose & Church)
- Unsupervised algorithms: Brill Tagger (Transformation-based learning), Constraint Grammar, Forward-Backward
Challenges / Issues
Some major challenges in POS Tagging
Example Systems
- ...
References / Links
- Wikipedia article on Part of Speech Tagging - [1]
