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Motivated by an application to unsupervised part-of-speech tagging, we present an algorithm for the Euclidean embedding of large sets of categorical data based on co-occurrence statistics. We use the CODE model of Globerson et al. but constrain the embedding to lie on a hig h-dimensional unit sphere. This constraint allows for efficient optimization, even(More)
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 implemented here, it achieves state-of-the-art tagging accuracy at considerably less cost than more recent methods. It can also(More)
We present a novel approach to distributional-only, fully unsupervised, POS tagging, based on an adaptation of the EM algorithm for the estimation of a Gaussian mixture. In this approach, which we call Latent-Descriptor Clustering (LDC), word types are clustered using a series of progressively more informative descriptor vectors. These descriptors, which(More)
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