2009 DistributionalRepresentationsfo

(Huang & Yates, 2009) ⇒ Fei Huang, and Alexander Yates. (2009). “Distributional Representations for Handling Sparsity in Supervised Sequence-labeling.” In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP.

Subject Headings: Supervised Sequence Labeling Algorithm

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Abstract

Supervised sequence-labeling systems in natural language processing often suffer from data sparsity because they use word types as features in their prediction tasks. Consequently, they have difficulty estimating parameters for types which appear in the test set, but seldom (or never) appear in the training set. We demonstrate that distributional representations of word types, trained on unannotated text, can be used to improve performance on rare words. We incorporate aspects of these representations into the feature space of our sequence-labeling systems. In an experiment on a standard chunking dataset, our best technique improves a chunker from 0.76 F1 to 0.86 F1 on chunks beginning with rare words. On the same dataset, it improves our part-of-speech tagger from 74% to 80% accuracy on rare words. Furthermore, our system improves significantly over a baseline system when applied to text from a different domain, and it reduces the sample complexity of sequence labeling.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 DistributionalRepresentationsfo	Alexander Yates Fei Huang			Distributional Representations for Handling Sparsity in Supervised Sequence-labeling						2009