The syntactic process
Identifying semantic roles using Combinatory Categorial Grammar
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Parsing the WSJ using CCG and log-linear models
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Evaluating the accuracy of an unlexicalized statistical parser on the PARC DepBank
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Brutus: a semantic role labeling system incorporating CCG, CFG, and dependency features
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
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
Developing a robust part-of-speech tagger for biomedical text
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Cross-genre and cross-domain detection of semantic uncertainty
Computational Linguistics
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Our CoNLL-2010 speculative sentence detector disambiguates putative keywords based on the following considerations: a speculative keyword may be composed of one or more word tokens; a speculative sentence may have one or more speculative keywords; and if a sentence contains at least one real speculative keyword, it is deemed speculative. A tree kernel classifier is used to assess whether a potential speculative keyword conveys speculation. We exploit information implicit in tree structures. For prediction efficiency, only a segment of the whole tree around a speculation keyword is considered, along with morphological features inside the segment and information about the containing document. A maximum entropy classifier is used for sentences not covered by the tree kernel classifier. Experiments on the Wikipedia data set show that our system achieves 0.55 F-measure (in-domain).