Abstract
Nearly all work in unsupervised grammar induction aims to induce unlabeled dependency trees from gold part-of-speechtagged text. These clean linguistic classes provide a very important, though unrealistic, inductive bias. Conversely, induced clusters are very noisy. We show here, for the first time, that very limited human supervision (three frequent words per cluster) may be required to induce labeled dependencies from automatically induced word clusters.
Cite
CITATION STYLE
Bisk, Y., Christodoulopoulos, C., & Hockenmaier, J. (2015). Labeled grammar induction with minimal supervision. In ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference (Vol. 2, pp. 870–876). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/p15-2143
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