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76
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 59 (5 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
SOFIE: A SelfOrganizing Framework for Information Extraction
 WWW 2009 MADRID! TRACK: SEMANTIC/DATA WEB / SESSION: LINKED DATA
, 2009
"... This paper presents SOFIE, a system for automated ontology extension. SOFIE can parse natural language documents, extract ontological facts from them and link the facts into an ontology. SOFIE uses logical reasoning on the existing knowledge and on the new knowledge in order to disambiguate words to ..."
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Cited by 41 (9 self)
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This paper presents SOFIE, a system for automated ontology extension. SOFIE can parse natural language documents, extract ontological facts from them and link the facts into an ontology. SOFIE uses logical reasoning on the existing knowledge and on the new knowledge in order to disambiguate words to their most probable meaning, to reason on the meaning of text patterns and to take into account world knowledge axioms. This allows SOFIE to check the plausibility of hypotheses and to avoid inconsistencies with the ontology. The framework of SOFIE unites the paradigms of pattern matching, word sense disambiguation and ontological reasoning in one unified model. Our experiments show that SOFIE delivers highquality output, even from unstructured Internet documents.
Factorie: Probabilistic programming via imperatively defined factor graphs
 In Advances in Neural Information Processing Systems 22
, 2009
"... Discriminatively trained undirected graphical models have had wide empirical success, and there has been increasing interest in toolkits that ease their application to complex relational data. The power in relational models is in their repeated structure and tied parameters; at issue is how to defin ..."
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Cited by 39 (7 self)
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Discriminatively trained undirected graphical models have had wide empirical success, and there has been increasing interest in toolkits that ease their application to complex relational data. The power in relational models is in their repeated structure and tied parameters; at issue is how to define these structures in a powerful and flexible way. Rather than using a declarative language, such as SQL or firstorder logic, we advocate using an imperative language to express various aspects of model structure, inference, and learning. By combining the traditional, declarative, statistical semantics of factor graphs with imperative definitions of their construction and operation, we allow the user to mix declarative and procedural domain knowledge, and also gain significant efficiencies. We have implemented such imperatively defined factor graphs in a system we call FACTORIE, a software library for an objectoriented, stronglytyped, functional language. In experimental comparisons to Markov Logic Networks on joint segmentation and coreference, we find our approach to be 315 times faster while reducing error by 2025%—achieving a new state of the art. 1
StatSnowball: a Statistical Approach to Extracting Entity Relationships
 WWW 2009 MADRID! TRACK: DATA MINING / SESSION: STATISTICAL METHODS
, 2009
"... Traditional relation extraction methods require prespecified relations and relationspecific humantagged examples. Bootstrapping systems significantly reduce the number of training examples, but they usually apply heuristicbased methods to combine a set of strict hard rules, which limit the abili ..."
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Cited by 34 (2 self)
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Traditional relation extraction methods require prespecified relations and relationspecific humantagged examples. Bootstrapping systems significantly reduce the number of training examples, but they usually apply heuristicbased methods to combine a set of strict hard rules, which limit the ability to generalize and thus generate a low recall. Furthermore, existing bootstrapping methods do not perform open information extraction (Open IE), which can identify various types of relations without requiring prespecifications. In this paper, we propose a statistical extraction framework called Statistical Snowball (StatSnowball), which is a bootstrapping system and can perform both traditional relation extraction and Open IE. StatSnowball uses the discriminative Markov logic networks
A General Method for Reducing the Complexity of Relational Inference And its Application to MCMC
"... Many realworld problems are characterized by complex relational structure, which can be succinctly represented in firstorder logic. However, many relational inference algorithms proceed by first fully instantiating the firstorder theory and then working at the propositional level. The applicabilit ..."
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Cited by 30 (4 self)
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Many realworld problems are characterized by complex relational structure, which can be succinctly represented in firstorder logic. However, many relational inference algorithms proceed by first fully instantiating the firstorder theory and then working at the propositional level. The applicability of such approaches is severely limited by the exponential time and memory cost of propositionalization. Singla and Domingos (2006) addressed this by developing a “lazy ” version of the WalkSAT algorithm, which grounds atoms and clauses only as needed. In this paper we generalize their ideas to a much broader class of algorithms, including other types of SAT solvers and probabilistic inference methods like MCMC. Lazy inference is potentially applicable whenever variables and functions have default values (i.e., a value that is much more frequent than the others). In relational domains, the default is false for atoms and true for clauses. We illustrate our framework by applying it to MCSAT, a stateoftheart MCMC algorithm. Experiments on a number of realworld domains show that lazy inference reduces both space and time by several orders of magnitude, making probabilistic relational inference applicable in previously infeasible domains.
Speeding up inference in Markov logic networks by preprocessing to reduce the size of the resulting grounded network. IJCAI09
"... Statisticalrelational reasoning has received much attention due to its ability to robustly model complex relationships. A key challenge is tractable inference, especially in domains involving many objects, due to the combinatorics involved. One can accelerate inference by using approximation techni ..."
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Cited by 19 (2 self)
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Statisticalrelational reasoning has received much attention due to its ability to robustly model complex relationships. A key challenge is tractable inference, especially in domains involving many objects, due to the combinatorics involved. One can accelerate inference by using approximation techniques, “lazy ” algorithms, etc. We consider Markov Logic Networks (MLNs), which involve counting how often logical formulae are satisfied. We propose a preprocessing algorithm that can substantially reduce the effective size of MLNs by rapidly counting how often the evidence satisfies each formula, regardless of the truth values of the query literals. This is a general preprocessing method that loses no information and can be used for any MLN inference algorithm. We evaluate our algorithm empirically in three realworld domains, greatly reducing the work needed during subsequent inference. Such reduction might even allow exact inference to be performed when sampling methods would be otherwise necessary. 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 18 (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
Gradientbased boosting for Statistical Relational Learning: The Relational Dependency Network Case
, 2011
"... Abstract. Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are graphical models that extend dependency networks to relational domains. This higher expressivity, however, co ..."
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Cited by 16 (9 self)
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Abstract. Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are graphical models that extend dependency networks to relational domains. This higher expressivity, however, comes at the expense of a more complex modelselection problem: an unbounded number of relational abstraction levels might need to be explored. Whereas current learning approaches for RDNs learn a single probability tree per random variable, we propose to turn the problem into a series of relational functionapproximation problems using gradientbased boosting. In doing so, one can easily induce highly complex features over several iterations and in turn estimate quickly a very expressive model. Our experimental results in several different data sets show that this boosting method results in efficient learning of RDNs when compared to stateoftheart statistical relational learning approaches. 1
Using Wikipedia to Bootstrap Open Information Extraction
"... We often use ‘Data Management ’ to refer to the manipulation of relational or semistructured information, but much of the world’s data is unstructured, for example the vast amount of naturallanguage text on the Web. The ability to manage ..."
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Cited by 14 (0 self)
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We often use ‘Data Management ’ to refer to the manipulation of relational or semistructured information, but much of the world’s data is unstructured, for example the vast amount of naturallanguage text on the Web. The ability to manage