@article{TACL1063,
	author = {Berend, Gábor},
	title = {Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling},
	journal = {Transactions of the Association for Computational Linguistics},
	volume = {5},
	year = {2017},
	keywords = {},
	abstract = {In this paper we propose and carefully evaluate a sequence labeling framework which solely utilizes sparse indicator features derived from dense distributed word representations. The proposed model obtains (near) state-of-the art performance for both part-of-speech tagging and named entity recognition for a variety of languages. Our model relies only on a few thousand sparse coding-derived features, without applying any modification of the word representations employed for the different tasks. The proposed model has favorable generalization properties as it retains over 89.8% of its average POS tagging accuracy when trained at 1.2% of the total available training data, i.e. 150 sentences per language.},
	issn = {2307-387X},
	url = {https://transacl.org/ojs/index.php/tacl/article/view/1063},
	pages = {247--261}
}