POS Induction (State of the art)
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.
|System name||Short description||Main publications||Software||Many-to-1|
|Brown+proto||MRF initialized with Brown prototypes||Christodoulopoulos, Goldwater and Steedman (2010)||sbo||76.1%|
|sbobet||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)
- 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.