We present a simple, but surprisingly effective, method of self-training a twophase parser-reranker system using readily available unlabeled data. We show that this type of bootstrapping is possible for parsing when the bootstrapped parses are processed by a discriminative reranker. Our improved model achieves an f-score of 92.1%, an absolute 1.1 % improvement (12 % error reduction) over the previous best result for Wall Street Journal parsing. Finally, we provide some analysis to better understand the phenomenon. 1

We present a practical co-training method for bootstrapping statistical parsers using a small amount of manually parsed training material and a much larger pool of raw sentences. Experimental results show that unlabelled sentences can be used to improve the performance of statistical parsers. In addition, we consider the problem of bootstrapping parsers when the manually parsed training material is in a different domain to either the raw sentences or the testing material. We show that bootstrapping continues to be useful, even though no manually produced parses from the target domain are used. 1

In the multi-view learning paradigm, the input variable is partitioned into two different views X1 and X2 and there is a target variable Y of interest. The underlying assumption is that either view alone is sufficient to predict the target Y accurately. This provides a natural semi-supervised learning setting in which unlabeled data can be used to eliminate hypothesis from either view, whose predictions tend to disagree with predictions based on the other view. This work explicitly formalizes an information theoretic, multi-view assumption and studies the multi-view paradigm in the PAC style semisupervised framework of Balcan and Blum [2006]. Underlying the PAC style framework is that an incompatibility function is assumed to be known — roughly speaking, this incompatibility function is a means to score how good a function is based on the unlabeled data alone. Here, we show how to derive incompatibility functions for certain loss functions of interest, so that minimizing this incompatibility over unlabeled data helps reduce expected loss on future test cases. In particular, we show how the class of empirically successful coregularization algorithms fall into our framework and provide performance bounds (using the results in Rosenberg and Bartlett [2007], Farquhar et al. [2005]). We also provide a normative justification for canonical correlation analysis (CCA) as a dimensionality reduction technique. In particular, we show (for strictly convex loss functions of the form ℓ(w·x, y)) that we can first use CCA as dimensionality reduction technique and (if the multi-view assumption is satisfied) this projection does not throw away much predictive information about the target Y — the benefit being that subsequent learning with a labeled set need only work in this lower dimensional space. 1

Labels are often expensive to get, and this motivates active learning which chooses the most informative samples for label acquisition. In this paper we study active sensing in a multi-view setting, motivated from many problems where grouped features are also expensive to obtain and need to be acquired (or sensed) actively (e.g., in cancer diagnosis each patient might go through many tests such as CT, Ultrasound and MRI to get valuable features). The strength of this model is that one actively sensed (sample, view) pair would improve the joint multi-view classification on all the samples. For this purpose we extend the Bayesian co-training framework such that it can handle missing views in a principled way, and introduce two criteria for view acquisition. Experiments on one toy data and two real-world medical problems show the effectiveness of this model. 1

We present a simple, but surprisingly effective, method of self-training a twophase parser-reranker system using readily available unlabeled data. We show that this type of bootstrapping is possible for parsing when the bootstrapped parses are processed by a discriminative reranker.