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We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models. Conditional random fields also avoid(More)
Recent approaches to text classification have used two different first-order probabilistic models for classification , both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey and Croft 1996; Koller and Sahami 1997). Others(More)
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large quantities of unlabeled documents are readily available. We(More)
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech tagging, text segmentation and information extraction. In these cases, the observations are usually mod-eled as multinomial distributions over a discrete vocabulary, and the HMM(More)
As Vice President of Research & Development, set research direction, identified key research problems and technical approaches, managed large group of research scientists, performed research and implementation, published research papers, gave invited talks at universities and conferences, co-architected object-oriented foundation classes for machine(More)
R in sample Vol. xx, No xx (xxxx) 1–87 c xxxx xxxxxxxxx DOI: xxxxxx Abstract Often we wish to predict a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination of classification and graph-ical modeling, combining the ability of graphical models to compactly model(More)
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce version space size. These other methods are popular because for many learning models, closed form calculation of the expected future error is intractable. Our approach is made feasible(More)
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when long-range dependencies exist. We present <i>dynamic conditional random fields (DCRFs)</i>, a generalization of linear-chain conditional random fields (CRFs) in which each time slice(More)
Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of training documents requires approximate inference techniques that are computationally expensive. With today's large-scale, constantly expanding document collections, it is useful(More)