Michael Wick


2012

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A Discriminative Hierarchical Model for Fast Coreference at Large Scale
Michael Wick | Sameer Singh | Andrew McCallum
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Human-Machine Cooperation: Supporting User Corrections to Automatically Constructed KBs
Michael Wick | Karl Schultz | Andrew McCallum
Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX)

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Monte Carlo MCMC: Efficient Inference by Sampling Factors
Sameer Singh | Michael Wick | Andrew McCallum
Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX)

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Monte Carlo MCMC: Efficient Inference by Approximate Sampling
Sameer Singh | Michael Wick | Andrew McCallum
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2008

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A Corpus for Cross-Document Co-reference
David Day | Janet Hitzeman | Michael Wick | Keith Crouch | Massimo Poesio
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper describes a newly created text corpus of news articles that has been annotated for cross-document co-reference. Being able to robustly resolve references to entities across document boundaries will provide a useful capability for a variety of tasks, ranging from practical information retrieval applications to challenging research in information extraction and natural language understanding. This annotated corpus is intended to encourage the development of systems that can more accurately address this problem. A manual annotation tool was developed that allowed the complete corpus to be searched for likely co-referring entity mentions. This corpus of 257K words links mentions of co-referent people, locations and organizations (subject to some additional constraints). Each of the documents had already been annotated for within-document co-reference by the LDC as part of the ACE series of evaluations. The annotation process was bootstrapped with a string-matching-based linking procedure, and we report on some of initial experimentation with the data. The cross-document linking information will be made publicly available.

2007

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First-Order Probabilistic Models for Coreference Resolution
Aron Culotta | Michael Wick | Andrew McCallum
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

2006

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Learning Field Compatibilities to Extract Database Records from Unstructured Text
Michael Wick | Aron Culotta | Andrew McCallum
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing