POS Induction (State of the art)

Evaluation

Many-to-1: Map every induced label to a gold standard tag greedily (45 labels to 45 tags of the Penn tag set). Use the mapping to compute tag accuracy on the Wall Street Journal portion of the Penn TreeBank.

Results

System name	Short description	Main publications	Software	Many-to-1
UPOS	Learning Syntactic Categories Using Paradigmatic Representations of Word Context	Yatbaz et al. (2012)	upos	80.2%
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

• Mehmet Ali Yatbaz, Enis Sert and Deniz Yuret. 2012. Learning Syntactic Categories Using Paradigmatic Representations of Word Context. In Proceedings of EMNLP 2012, pages 940-951.

• 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