On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Maximum Entropy Modeling with Clausal Constraints
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Machine Learning
Discriminative training of Markov logic networks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Lifted first-order probabilistic inference
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
BLOG: probabilistic models with unknown objects
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Using decision trees for conference resolution
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Probabilistic Quantifier Logic for General Intelligence: An Indefinite Probabilities Approach
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Learning field compatibilities to extract database records from unstructured text
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A discriminative hierarchical model for fast coreference at large scale
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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Markov logic is a highly expressive language recently introduced to specify the connectivity of a Markov network using first-order logic. While Markov logic is capable of constructing arbitrary first-order formulae over the data, the complexity of these formulae is often limited in practice because of the size and connectivity of the resulting network. In this paper, we present approximate inference and estimation methods that incrementally instantiate portions of the network as needed to enable first-order existential and universal quantifiers in Markov logic networks. When applied to the problem of identity uncertainty, this approach results in a conditional probabilistic model that can reason about objects, combining the expressivity of recently introduced BLOG models with the predictive power of conditional training. We validate our algorithms on the tasks of citation matching and author disambiguation.