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Bayesian Modeling of Lexical Resources for Low-Resource Settings
2017
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Lexical resources such as dictionaries and gazetteers are often used as auxiliary data for tasks such as part-of-speech induction and named-entity recognition. However, discriminative training with lexical features requires annotated data to reliably estimate the lexical feature weights and may result in overfitting the lexical features at the expense of features which generalize better. In this paper, we investigate a more robust approach: we stipulate that the lexicon is the result of an
doi:10.18653/v1/p17-1095
dblp:conf/acl/AndrewsDDE17
fatcat:zpg6zlqkjfel5mxnr3t6nlgayq