POS Induction (State of the art)

Evaluation

Many-to-1: Mapping every induced label to a gold standard tag greedily. Use the mapping to compute tag accuracy on the Wall Street Journal part of the Penn TreeBank.

Results

System name	Short description	Main publications	Software	Many-to-1
Brown+proto	MRF initialized with Brown prototypes	Christodoulopoulos, Goldwater and Steedman (2010)		76.1%
	Logistic regression with features and LBFGS	Berg-Kirkpatrick et al. (2010)		75.5%
Clark DMF	Distributional clustering + morphology + frequency	Clark (2003)	alexc	71.2%*

* according to Christodoulopoulos, Goldwater and Steedman (2010)

References

• Christos Christodoulopoulos, Sharon Goldwater and Mark Steedman. 2010. Two Decades of Unsupervised POS induction: How far have we come? In Proceedings of EMNLP 2010.

• Taylor Berg-Kirkpatrick, Alexandre Bouchard-Cote, John DeNero, and Dan Klein. 2010. Painless Unsupervised Learning with Features. NAACL 2010.

• Alexander Clark. 2003. Combining distributional and morphological information for part of speech induction. In Proceedings of EACL 2003, pages 59–66, Morristown, NJ, USA.

See also

• POS Tagging (State of the art)
• Part-of-speech tagging
• State of the art