@article{TACL837,
	author = {Stratos, Karl  and Collins, Michael  and Hsu, Daniel },
	title = {Unsupervised Part-Of-Speech Tagging with Anchor Hidden Markov Models},
	journal = {Transactions of the Association for Computational Linguistics},
	volume = {4},
	year = {2016},
	keywords = {},
	abstract = {We tackle unsupervised part-of-speech (POS) tagging by learning hidden Markov models (HMMs) that are particularly well-suited for the problem. These HMMs, which we call anchor HMMs, assume that each tag is associated with at least one word that can have no other tag, which is a relatively benign condition for POS tagging (e.g., \"the\" is a word that appears only under the determiner tag). We exploit this assumption and extend the non-negative matrix factorization framework of Arora et al. (2012) to design a consistent estimator for anchor HMMs. In experiments, our algorithm is competitive with strong baselines such as the clustering method of Brown et al. (1992) and the log-linear model of Berg-Kirkpatrick et al. (2010). Furthermore, it produces an interpretable model in which hidden states are automatically lexicalized by words.},
	issn = {2307-387X},
	url = {https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/837},
	pages = {245--257}
}