Making large-scale support vector machine learning practical
Advances in kernel methods
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Novel semantic features for verb sense disambiguation
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Combining knowledge- and corpus-based word-sense-disambiguation methods
Journal of Artificial Intelligence Research
NeSp-NLP '10 Proceedings of the Workshop on Negation and Speculation in Natural Language Processing
Automatic annotation of speculation in biomedical texts: new perspectives and large-scale evaluation
NeSp-NLP '10 Proceedings of the Workshop on Negation and Speculation in Natural Language Processing
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
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
A high-precision approach to detecting hedges and their scopes
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
Exploiting CCG structures with tree kernels for speculation detection
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
A simple ensemble method for hedge identification
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
A unified framework for scope learning via simplified shallow semantic parsing
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Learning the scope of negation via shallow semantic parsing
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
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
Semantic representation of negation using focus detection
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Automatic extraction of lexico-syntactic patterns for detection of negation and speculation scopes
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Learning local content shift detectors from document-level information
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Modality and negation: An introduction to the special issue
Computational Linguistics
Are you sure that this happened? assessing the factuality degree of events in text
Computational Linguistics
Cross-genre and cross-domain detection of semantic uncertainty
Computational Linguistics
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Distinguishing speculative statements from factual ones is important for most biomedical text mining applications. We introduce an approach which is based on solving two sub-problems to identify speculative sentence fragments. The first sub-problem is identifying the speculation keywords in the sentences and the second one is resolving their linguistic scopes. We formulate the first sub-problem as a supervised classification task, where we classify the potential keywords as real speculation keywords or not by using a diverse set of linguistic features that represent the contexts of the keywords. After detecting the actual speculation keywords, we use the syntactic structures of the sentences to determine their scopes.