Making large-scale support vector machine learning practical
Advances in kernel methods
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Identifying Document Topics Using the Wikipedia Category Network
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Overview of BioNLP'09 shared task on event extraction
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Syntactic dependency based heuristics for biological event extraction
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
ConText: an algorithm for identifying contextual features from clinical text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
A shared task involving multi-label classification of clinical free text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Joint memory-based learning of syntactic and semantic dependencies in multiple languages
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Detecting speculations and their scopes in scientific text
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
The CoNLL-2010 shared task: learning to detect hedges and their scope in natural language text
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
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Information-oriented document labeling is a special document multi-labeling task where the target labels refer to a specific information instead of the topic of the whole document. These kind of tasks are usually solved by looking up indicator phrases and analyzing their local context to filter false positive matches. Here, we introduce an approach for machine learning local content shifters which detects irrelevant local contexts using just the original document-level training labels. We handle content shifters in general, instead of learning a particular language phenomenon detector (e.g. negation or hedging) and form a single system for document labeling and content shift detection. Our empirical results achieved 24% error reduction -- compared to supervised baseline methods -- on three document labeling tasks.