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63
Joint Unsupervised Coreference Resolution with Markov Logic
"... Machine learning approaches to coreference resolution are typically supervised, and require expensive labeled data. Some unsupervised approaches have been proposed (e.g., Haghighi and Klein (2007)), but they are less accurate. In this paper, we present the first unsupervised approach that is competi ..."
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Cited by 62 (6 self)
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Machine learning approaches to coreference resolution are typically supervised, and require expensive labeled data. Some unsupervised approaches have been proposed (e.g., Haghighi and Klein (2007)), but they are less accurate. In this paper, we present the first unsupervised approach that is competitive with supervised ones. This is made possible by performing joint inference across mentions, in contrast to the pairwise classification typically used in supervised methods, and by using Markov logic as a representation language, which enables us to easily express relations like apposition and predicate nominals. On MUC and ACE datasets, our model outperforms Haghigi and Klein’s one using only a fraction of the training data, and often matches or exceeds the accuracy of stateoftheart supervised models. 1
Discriminative Structure and Parameter Learning for Markov Logic Networks
"... Markov logic networks (MLNs) are an expressive representation for statistical relational learning that generalizes both firstorder logic and graphical models. Existing methods for learning the logical structure of an MLN are not discriminative; however, many relational learning problems involve spe ..."
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Cited by 35 (5 self)
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Markov logic networks (MLNs) are an expressive representation for statistical relational learning that generalizes both firstorder logic and graphical models. Existing methods for learning the logical structure of an MLN are not discriminative; however, many relational learning problems involve specific target predicates that must be inferred from given background information. We found that existing MLN methods perform very poorly on several such ILP benchmark problems, and we present improved discriminative methods for learning MLN clauses and weights that outperform existing MLN and traditional ILP methods. 1.
Hybrid Markov Logic Networks
"... Markov logic networks (MLNs) combine firstorder logic and Markov networks, allowing us to handle the complexity and uncertainty of realworld problems in a single consistent framework. However, in MLNs all variables and features are discrete, while most realworld applications also contain continuo ..."
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Cited by 29 (1 self)
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Markov logic networks (MLNs) combine firstorder logic and Markov networks, allowing us to handle the complexity and uncertainty of realworld problems in a single consistent framework. However, in MLNs all variables and features are discrete, while most realworld applications also contain continuous ones. In this paper we introduce hybrid MLNs, in which continuous properties (e.g., the distance between two objects) and functions over them can appear as features. Hybrid MLNs have all distributions in the exponential family as special cases (e.g., multivariate Gaussians), and allow much more compact modeling of noni.i.d. data than propositional representations like hybrid Bayesian networks. We also introduce inference algorithms for hybrid MLNs, by extending the MaxWalkSAT and MCSAT algorithms to continuous domains. Experiments in a mobile robot mapping domain—involving joint classification, clustering and regression—illustrate the power of hybrid MLNs as a modeling language, and the accuracy and efficiency of the inference algorithms.
Deep transfer via secondorder markov logic
 In Proceedings of the AAAI Workshop on Transfer Learning For Complex Tasks
, 2008
"... Standard inductive learning requires that training and test instances come from the same distribution. Transfer learning seeks to remove this restriction. In shallow transfer, test instances are from the same domain, but have a different distribution. In deep transfer, test instances are from a diff ..."
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Cited by 23 (3 self)
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Standard inductive learning requires that training and test instances come from the same distribution. Transfer learning seeks to remove this restriction. In shallow transfer, test instances are from the same domain, but have a different distribution. In deep transfer, test instances are from a different domain entirely (i.e., described by different predicates). Humans routinely perform deep transfer, but few learning systems, if any, are capable of it. In this paper we propose an approach based on a form of secondorder Markov logic. Our algorithm discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain. Using this approach, we have successfully transferred learned knowledge among molecular biology, social network and Web domains. The discovered patterns include broadly useful properties of predicates, like symmetry and transitivity, and relations among predicates, such as various forms of homophily. 1.
Maxmargin weight learning for Markov logic networks
 In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD09). Bled
, 2009
"... Abstract. Markov logic networks (MLNs) are an expressive representation for statistical relational learning that generalizes both firstorder logic and graphical models. Existing discriminative weight learning methods for MLNs all try to learn weights that optimize the Conditional Log Likelihood (CL ..."
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Cited by 17 (5 self)
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Abstract. Markov logic networks (MLNs) are an expressive representation for statistical relational learning that generalizes both firstorder logic and graphical models. Existing discriminative weight learning methods for MLNs all try to learn weights that optimize the Conditional Log Likelihood (CLL) of the training examples. In this work, we present a new discriminative weight learning method for MLNs based on a maxmargin framework. This results in a new model, MaxMargin Markov Logic Networks (M3LNs), that combines the expressiveness of MLNs with the predictive accuracy of structural Support Vector Machines (SVMs). To train the proposed model, we design a new approximation algorithm for lossaugmented inference in MLNs based on Linear Programming (LP). The experimental result shows that the proposed approach generally achieves higher F1 scores than the current best discriminative weight learner for MLNs. 1
Structure learning of Markov logic networks through iterated local search
 Proc. ECAI’08
, 2008
"... Many realworld applications of AI require both probability and firstorder logic to deal with uncertainty and structural complexity. Logical AI has focused mainly on handling complexity, and statistical AI on handling uncertainty. Markov Logic Networks (MLNs) are a powerful representation that comb ..."
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Cited by 17 (2 self)
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Many realworld applications of AI require both probability and firstorder logic to deal with uncertainty and structural complexity. Logical AI has focused mainly on handling complexity, and statistical AI on handling uncertainty. Markov Logic Networks (MLNs) are a powerful representation that combine Markov Networks (MNs) and firstorder logic by attaching weights to firstorder formulas and viewing these as templates for features of MNs. Stateoftheart structure learning algorithms of MLNs maximize the likelihood of a relational database by performing a greedy search in the space of candidates. This can lead to suboptimal results because of the incapability of these approaches to escape local optima. Moreover, due to the combinatorially explosive space of potential candidates these methods are computationally prohibitive. We propose a novel algorithm for learning MLNs structure, based on the Iterated Local Search (ILS) metaheuristic that explores the space of structures through a biased sampling of the set of local optima. The algorithm focuses the search not on the full space of solutions but on a smaller subspace defined by the solutions that are locally optimal for the optimization engine. We show through experiments in two realworld domains that the proposed approach improves accuracy and learning time over the existing stateoftheart algorithms. 1
Learning and Inference in WEIGHTED LOGIC WITH APPLICATION TO NATURAL LANGUAGE PROCESSING
, 2008
"... ..."
Hingeloss Markov Random Fields: Convex Inference for Structured Prediction
 In Uncertainty in Artificial Intelligence
, 2013
"... Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable. Instead of working in a combinatorial space, we use hingeloss Markov random ..."
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Cited by 7 (7 self)
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Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable. Instead of working in a combinatorial space, we use hingeloss Markov random fields (HLMRFs), an expressive class of graphical models with logconcave density functions over continuous variables, which can represent confidences in discrete predictions. This paper demonstrates that HLMRFs are general tools for fast and accurate structured prediction. We introduce the first inference algorithm that is both scalable and applicable to the full class of HLMRFs, and show how to train HLMRFs with several learning algorithms. Our experiments show that HLMRFs match or surpass the predictive performance of stateoftheart methods, including discrete models, in four application domains. 1
Online MaxMargin Weight Learning for Markov Logic Networks
"... Most of the existing weightlearning algorithms for Markov Logic Networks (MLNs) use batch training which becomes computationally expensive and even infeasible for very large datasets since the training examples may not fit in main memory. To overcome this problem, previous work has used online lear ..."
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Cited by 7 (1 self)
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Most of the existing weightlearning algorithms for Markov Logic Networks (MLNs) use batch training which becomes computationally expensive and even infeasible for very large datasets since the training examples may not fit in main memory. To overcome this problem, previous work has used online learning algorithms to learn weights for MLNs. However, this prior work has only applied existing online algorithms, and there is no comprehensive study of online weight learning for MLNs. In this paper, we derive a new online algorithm for structured prediction using the primaldual framework, apply it to learn weights for MLNs, and compare against existing online algorithms on three large, realworld datasets. The experimental results show that our new algorithm generally achieves better accuracy than existing methods, especially on noisy datasets.
Transfer in Reinforcement Learning via Markov Logic Networks
"... We propose the use of statistical relational learning, and in particular the formalism of Markov Logic Networks, for transfer in reinforcement learning. Our goal is to extract relational knowledge from a source task and use it to speed up learning in a related target task. We do so by learning a Mar ..."
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Cited by 7 (4 self)
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We propose the use of statistical relational learning, and in particular the formalism of Markov Logic Networks, for transfer in reinforcement learning. Our goal is to extract relational knowledge from a source task and use it to speed up learning in a related target task. We do so by learning a Markov Logic Network that describes the sourcetask Qfunction, and then using it for decision making in the early learning stages of the target task. Through experiments in the RoboCup simulatedsoccer domain, we show that this approach can provide a substantial performance benefit in the target task.