Part of Speech Tagging
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Part of Speech Tagging (or POS Tagging for short) is a problem 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 by DeRose & Church, mentioned for historical reasons
- YFCL
Sources of information/evidence often times used by POS taggers:
- The 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
References / Links
- Webpage with links to many different POS tagger systems, from Statistical natural language processing and corpus-based computational linguistics: An annotated list of resources - [1]
- Wikipedia article on Part of Speech Tagging - [2]
- CMU Algorithms for NLP notes on POS Tagging - [3]