Practical Markov Logic Containing First-order Quantifiers with Application to Identity Uncertainty. Culotta, A. & McCallum, A. In *HLT Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing*, 2006.

Paper bibtex

Paper bibtex

@inproceedings{DBLP:conf/hlt_ws/Culotta06, author = {Aron Culotta and Andrew McCallum}, title = {Practical Markov Logic Containing First-order Quantifiers with Application to Identity Uncertainty}, booktitle = {HLT Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing}, year = {2006}, url = {https://people.cs.umass.edu/~mccallum/papers/practical-hltws06.pdf}, sum = {Markov Logic Networks are Conditional Random Fields that use first-order logic to define features and parameter tying patterns. Making such models scale to non-trivial data set sizes is a challenge because the size of the full instantiation of the model is exponential in the arity of the formulae. Here we describe a method of partial instantiation that allows such models to scale to entity resolution problems millions of entity mentions. On both citation and author entity resolution problems we show that inclusing such first-order features provides increases in accuracy.}, }

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