Sander Canisius Research page

Publications

Machine learning | Information extraction | Parsing | Other topics

Machine learning

Constraint Satisfaction Inference: Non-probabilistic Global Inference for Sequence Labelling
Authors:Sander Canisius, Antal van den Bosch, and Walter Daelemans
Reference:In Proceedings of the EACL 2006 Workshop on Learning Structured Information in Natural Language Applications, Trento, April 2006.
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Rule meta-learning for trigram-based sequence processing
Authors:Sander Canisius, Antal van den Bosch, and Walter Daelemans
Reference:In J. Cussens and C. Nedellec (Eds.), Proceedings of the Fourth Learning Language in Logic Workshop, pp. 3-10, Bonn, August 2005.
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Information extraction

Bootstrapping Information Extraction from Field Books
Authors:Sander Canisius and Caroline Sporleder
Reference:In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Prague, Czech Republic.
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Learning to Segment and Label Semi-Structured Documents with Little or No Supervision
Authors:Sander Canisius and Caroline Sporleder
Reference:In Proceedings of Benelearn 2007, Amsterdam, The Netherlands.
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Discrete versus Probabilistic Sequence Classifiers for Domain-specific Entity Chunking
Authors:Sander Canisius, Antal van den Bosch, and Walter Daelemans
Reference:In Proceedings of BNAIC 2006, Namur, Belgium.
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Parsing

Recompiling a knowledge-based dependency parser into memory
Authors:Sander Canisius and Antal van den Bosch
Reference:In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP-2007), Borovets, Bulgaria.
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A Constraint Satisfaction Approach to Dependency Parsing
Authors:Sander Canisius and Erik Tjong Kim Sang
Reference:In Proceedings of the CoNLL Shared Task Session of EMNLP-CoNLL 2007, Prague, Czech Republic.
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Dependency Parsing by Inference over High-recall Dependency Predictions
Authors:Sander Canisius, Toine Bogers, Antal van den Bosch, Jeroen Geertzen, and Erik Tjong Kim Sang
Reference:In Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X), New York, USA.
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A memory-based shallow parser for spoken Dutch
Authors:Sander Canisius and Antal van den Bosch
Reference:In B. Decadt, G. De Pauw, and V. Hoste (Eds.), Selected papers from the Fourteenth Computational Linguistics in the Netherlands Meeting, Antwerp, Belgium, pp. 31-45.
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Memory-Based Shallow Parsing of Spoken Dutch
Authors:Sander Canisius
Reference:Master thesis, Universiteit Maastricht
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Other topics

An efficient memory-based morpho-syntactic tagger and parser for Dutch
Authors:Antal van den Bosch, Bertjan Busser, Sander Canisius, and Walter Daelemans
Reference:In P. Dirix, I. Schuurman, V. Vandeghinste, and F. Van Eynde (Eds.), Computational Linguistics in the Netherlands: Selected Papers from the Seventeenth CLIN Meeting, Leuven, Belgium, pp. 99-114.
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Improved morpho-phonological sequence processing with constraint satisfaction inference
Authors:Antal van den Bosch and Sander Canisius
Reference:In Proceedings of the Eighth Meeting of the ACL Special Interest Group on Computational Phonology and Morphology at HLT-NAACL 2006, New York, USA.
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Applying spelling error correction techniques for improving semantic role labeling
Authors:Erik Tjong Kim Sang, Sander Canisius, Antal van den Bosch, and Toine Bogers
Reference:In Proceedings of the Ninth Conference on Natural Language Learning, CoNLL-2005, June 29-30, 2005, Ann Arbor, MI.
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Memory-based robust interpretation of recognised speech
Authors:Piroska Lendvai, Antal van den Bosch, Emiel Krahmer, and Sander Canisius
Reference:In Proceedings of the 9th International Conference on "Speech and Computer", SPECOM'04, St. Petersburg, Russia, pp. 415-422.
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Memory-based semantic role labeling: Optimizing features, algorithm, and output
Authors:Antal van den Bosch, Sander Canisius, Walter Daelemans, Iris Hendrickx, and Erik Tjong Kim Sang
Reference:In H.T. Ng and E. Riloff (Eds.), Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-2004), Boston, MA, USA.
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