Difference between revisions of "Part of Speech Tagging"
From Cohen Courses
Jump to navigationJump to searchPastStudents (talk | contribs) |
PastStudents (talk | contribs) |
||
Line 26: | Line 26: | ||
* Webpage with links to many different POS tagger systems, from Statistical natural language processing and corpus-based computational linguistics: An annotated list of resources - [http://www-nlp.stanford.edu/links/statnlp.html#Taggers] | * Webpage with links to many different POS tagger systems, from Statistical natural language processing and corpus-based computational linguistics: An annotated list of resources - [http://www-nlp.stanford.edu/links/statnlp.html#Taggers] | ||
* 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] | ||
+ | * CMU Algorithms for NLP notes on POS Tagging - [http://www.cs.cmu.edu/afs/cs.cmu.edu/project/cmt-55/lti/Courses/711/Class-notes/POS-tagging.pdf] |
Revision as of 19:31, 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 each word in a text corpus with the associated word categories known as parts of speech (such as noun, verb, or adjective), based on a word's definition and context of usage.
POS tagging can be useful as a preprocessing step in tasks like Parsing, and is also useful in tasks like Word Sense Disambiguation and Speech Synthesis.
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
- Transformation-based learning - Brill Tagger
- Dynamic Programming/Viterbi-like algorithms - DeRose & Church, mentioned for historical reasons
Sources of information/evidence often times used by POS taggers:
- distribution of tags for the word isolation: P(t|w)
- "Syntagmatic information"- some POS sequences are much more common than others due to syntactic constraints of the language
Example Systems
- FastTag - open source implementation of Brill Tagger
- Stanford Log-linear Part-of-Speech Tagger
- OpenNLP Tagger - based on maximum entropy
- CRF Tagger - based on conditional random fields
- LingPipe - tool kit that contains models for POS tagging