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87
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 84 (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 56 (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 42 (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 41 (4 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 30 (6 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
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 26 (18 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
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 25 (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
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Elementary: Largescale Knowledgebase Construction via Machine Learning and Statistical Inference
"... Researchers have approached knowledgebase construction (KBC) with a wide range of data resources and techniques. We present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machine learning and statistical inference to construct knowledge ..."
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Cited by 14 (5 self)
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Researchers have approached knowledgebase construction (KBC) with a wide range of data resources and techniques. We present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machine learning and statistical inference to construct knowledge bases. Using Elementary, we have implemented a solution to the TACKBP challenge with quality comparable to the state of the art, as well as an endtoend online demonstration that automatically and continuously enriches Wikipedia with structured data by reading millions of webpages on a daily basis. We describe several challenges and our solutions in designing, implementing, and deploying Elementary. In particular, we first describe the conceptual framework and architecture of Elementary, and then discuss how we address scalability challenges to enable Webscale deployment. First, to take advantage of diverse data resources and proven techniques, Elementary employs Markov logic, a succinct yet expressive language to specify probabilistic graphical models. Elementary accepts both domainknowledge rules and classical machinelearning models such as conditional random fields, thereby integrating different data resources and KBC techniques in a principled manner. Second, to support largescale KBC with terabytes of data and millions of entities, Elementary
RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models
 In Proceedings of the 27th Conference on Artificial Intelligence (AAAI
"... ROCKIT is a maximum aposteriori (MAP) query engine for statistical relational models. MAP inference in graphical models is an optimization problem which can be compiled to integer linear programs (ILPs). We describe several advances in translating MAP queries to ILP instances and present the novel ..."
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Cited by 13 (3 self)
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ROCKIT is a maximum aposteriori (MAP) query engine for statistical relational models. MAP inference in graphical models is an optimization problem which can be compiled to integer linear programs (ILPs). We describe several advances in translating MAP queries to ILP instances and present the novel metaalgorithm cutting plane aggregation (CPA). CPA exploits local contextspecific symmetries and bundles up sets of linear constraints. The resulting counting constraints lead to more compact ILPs and make the symmetry of the ground model more explicit to stateoftheart ILP solvers. Moreover, ROCKIT parallelizes most parts of the MAP inference pipeline taking advantage of ubiquitous sharedmemory multicore architectures. We report on extensive experiments with Markov logic network (MLN) benchmarks showing that ROCKIT outperforms the stateoftheart systems ALCHEMY, MARKOV THEBEAST, and TUFFY both in terms of efficiency and quality of results.