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Large Margin Methods for Structured and Interdependent Output Variables
Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainlyExpand
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Support vector machine learning for interdependent and structured output spaces
Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input representations.Expand
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Hidden Markov Support Vector Machines
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines and Hidden MarkovExpand
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Relative Entropy Policy Search
Policy search is a successful approach to reinforcement learning. However, policy improvements often result in the loss of information. Hence, it has been marred by premature convergence andExpand
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Broad-Coverage Sense Disambiguation and Information Extraction with a Supersense Sequence Tagger
In this paper we approach word sense disambiguation and information extraction as a unified tagging problem. The task consists of annotating text with the tagset defined by the 41 Wordnet supersenseExpand
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Overcoming the Lack of Parallel Data in Sentence Compression
A major challenge in supervised sentence compression is making use of rich feature representations because of very scarce parallel data. We address this problem and present a method to automaticallyExpand
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Maximum Margin Semi-Supervised Learning for Structured Variables
Many real-world classification problems involve the prediction of multiple inter-dependent variables forming some structural dependency. Recent progress in machine learning has mainly focused onExpand
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Discovering emerging entities with ambiguous names
Knowledge bases (KB's) contain data about a large number of people, organizations, and other entities. However, this knowledge can never be complete due to the dynamics of the ever-changing world:Expand
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Unifying Divergence Minimization and Statistical Inference Via Convex Duality
In this paper we unify divergence minimization and statistical inference by means of convex duality. In the process of doing so, we prove that the dual of approximate maximum entropy estimation isExpand
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Investigating Loss Functions and Optimization Methods for Discriminative Learning of Label Sequences
Discriminative models have been of interest in the NLP community in recent years. Previous research has shown that they are advantageous over generative models. In this paper, we investigate howExpand
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