Difference between revisions of "Part of Speech Tagging"

From Cohen Courses
Jump to navigationJump to search
Line 6: Line 6:
  
 
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  
 
* Dynamic Programming/Viterbi-like algorithms (DeRose & Church)
 
* Dynamic Programming/Viterbi-like algorithms (DeRose & Church)
* ''Unsupervised'' algorithms: Brill Tagger (Transformation-based learning), Constraint Grammar, Forward-Backward
+
* Unsupervised approaches: Brill Tagger (Transformation-based learning), Constraint Grammar, Forward-Backward
 
 
== Challenges / Issues ==
 
 
 
Some major challenges in POS Tagging
 
  
 
== Example Systems ==
 
== Example Systems ==
* ...
+
* [http://www.markwatson.com/opensource/ FastTag] - open source implementation of Brill Tagger
 +
* [http://opennlp.sourceforge.net/ OpenNLP Tagger] - based on maximum entropy
 +
* [http://crftagger.sourceforge.net/ CRF Tagger] - based on conditional random fields
 +
* [http://alias-i.com/lingpipe/ LingPipe] - tool kit that contains models for POS tagging
  
 
== References / Links ==
 
== References / Links ==
 
* Wikipedia article on Part of Speech Tagging - [http://en.wikipedia.org/wiki/Part-of-speech_tagging]
 
* Wikipedia article on Part of Speech Tagging - [http://en.wikipedia.org/wiki/Part-of-speech_tagging]
 +
* Webpage with download links to many different POS taggers, from Statistical natural language processing and corpus-based computational linguistics: An annotated list of resources - [http://www-nlp.stanford.edu/links/statnlp.html#Taggers]

Revision as of 19:05, 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 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

References / Links

  • Wikipedia article on Part of Speech Tagging - [1]
  • Webpage with download links to many different POS taggers, from Statistical natural language processing and corpus-based computational linguistics: An annotated list of resources - [2]