Paper: Simple Type-Level Unsupervised POS Tagging

ACL ID D10-1083
Title Simple Type-Level Unsupervised POS Tagging
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

Part-of-speech (POS) tag distributions are known to exhibit sparsity — a word is likely to take a single predominant tag in a corpus. Recent research has demonstrated that incor- porating this sparsity constraint improves tag- ging accuracy. However, in existing systems, this expansion come with a steep increase in model complexity. This paper proposes a sim- ple and effective tagging method that directly models tag sparsity and other distributional properties of valid POS tag assignments. In addition, this formulation results in a dramatic reduction in the number of model parame- ters thereby, enabling unusually rapid training. Our experiments consistently demonstrate that this model architecture yields substantial per- formance gains over more complex tagging counterparts. On several l...