We propose a new method for unsupervised tagging that finds minimal models which are then further improved by Expectation Max-imization training. In contrast to previous approaches that rely on manually specified and multi-step heuristics for model minimiza-tion, our approach is a simple greedy

. In the address domain, separate tags for street names, locations and other address parts can be observed. To extract the addresses, we apply a Conditional Random Field (CRF) on a labeled training set of addresses, using the unsupervised tags as features. Evaluation on a gold standard of correctly annotated data

Model minimization has been shown to work well for the task of unsupervised part-of-speech tagging with a dictionary. In (Ravi and Knight, 2009), the authors invoke an integer programming (IP) solver to do model minimization. However, solving this problem exactly using an integer programming

whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from tagged sites on the social bookmarking site del.icio.us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use

In this paper we describe an unsupervised learning algorithm for automatically training a rule-based part of speech tagger without using a manually tagged corpus. We compare this algorithm to the Baum-Welch algorithm, used for unsupervised training of stochastic taggers. Next, we show a method

Unsupervised learning of linguistic structure is a difficult problem. A common approach is to define a generative model and maximize the probability of the hidden structure given the observed data. Typically, this is done using maximum-likelihood estimation (MLE) of the model parameters. We show

Different approaches have been taken in order to solve the part-of-speech tagging problem. Several methods for unsupervised tagging have obtained good accuracies in practice. The approach taken by Brill [Bri95] obtains results comparable to the best existing taggers. In this paper we explore

We demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech tagging. The key hypothesis of multilingual learning is that by combining cues from multiple languages, the structure of each becomes more apparent. We formulate a hierarchical Bayesian model for jointly

and methods for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering

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