Paper: SVD and Clustering for Unsupervised POS Tagging

ACL ID P10-2040
Title SVD and Clustering for Unsupervised POS Tagging
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2010
Authors

We revisit the algorithm of Schütze (1995) for unsupervised part-of-speech tagging. The algorithm uses reduced-rank singular value decomposition followed by clustering to extract latent features from context distributions. As imple- mented here, it achieves state-of-the-art tagging accuracy at considerably less cost than more recent methods. It can also produce a range of finer-grained tag- gings, with potential applications to vari- ous tasks.