Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
The use of bigrams to enhance text categorization
Information Processing and Management: an International Journal
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Augmenting Naive Bayes Classifiers with Statistical Language Models
Information Retrieval
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Weakly-supervised relation classification for information extraction
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Scaling to very very large corpora for natural language disambiguation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Weakly supervised natural language learning without redundant views
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Counter-training in discovery of semantic patterns
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Relation extraction using label propagation based semi-supervised learning
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Effective self-training for parsing
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
The second release of the RASP system
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
Weakly supervised learning methods for improving the quality of gene name normalization data
ISMB '05 Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics
Using pointwise mutual information to identify implicit features in customer reviews
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
Techniques for improving the performance of naive bayes for text classification
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Learning the scope of hedge cues in biomedical texts
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Journal of Biomedical Informatics
Exploring surface-level heuristics for negation and speculation discovery in clinical texts
BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
A hedgehop over a max-margin framework using hedge cues
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
Detecting hedge cues and their scopes with average perceptron
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
Memory-based resolution of in-sentence scopes of hedge cues
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
Uncertainty detection as approximate max-margin sequence labelling
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
Hedge detection and scope finding by sequence labeling with normalized feature selection
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
Exploiting multi-features to detect hedges and their scope in biomedical texts
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
A baseline approach for detecting sentences containing uncertainty
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
Detecting hedge cues and their scope in biomedical text with conditional random fields
Journal of Biomedical Informatics
Multiple attribute frequent mining-based for dengue outbreak
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Mining uncertain sentences with multiple instance learning
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
BioExcom: detection and categorization of speculative sentences in biomedical literature
LTC'09 Proceedings of the 4th conference on Human language technology: challenges for computer science and linguistics
A parser-based approach to detecting modification of biomedical events
Proceedings of the ACM fifth international workshop on Data and text mining in biomedical informatics
Modality and negation: An introduction to the special issue
Computational Linguistics
Kernel-Based logical and relational learning with klog for hedge cue detection
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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We investigate automatic identification of speculative language, or 'hedging', in scientific literature from the biomedical domain. Our contributions include a precise description of the task including annotation guidelines, theoretical analysis and discussion. We show that good agreement can be achieved using our guidelines and present a publicly available benchmark dataset for the task. We argue for separation of the acquisition and classification phases in semi-supervised machine learning, and present a probabilistic acquisition model which is evaluated both theoretically and experimentally. We explore the impact of different sample representations on classification accuracy across the learning curve and demonstrate the effectiveness of using machine learning for the hedge identification task. Finally, we examine the errors made by our approach and point toward avenues for future research.