Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The second release of the RASP system
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
Learning the scope of hedge cues in biomedical texts
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical 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
Syntactic scope resolution in uncertainty analysis
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
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
Cross-genre and cross-domain detection of semantic uncertainty
Computational Linguistics
Speculation and negation: Rules, rankers, and the role of syntax
Computational Linguistics
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Hedge cues were detected using a supervised Conditional Random Field (CRF) classifier exploiting features from the RASP parser. The CRF's predictions were filtered using known cues and unseen instances were removed, increasing precision while retaining recall. Rules for scope detection, based on the grammatical relations of the sentence and the part-of-speech tag of the cue, were manually-developed. However, another supervised CRF classifier was used to refine these predictions. As a final step, scopes were constructed from the classifier output using a small set of post-processing rules. Development of the system revealed a number of issues with the annotation scheme adopted by the organisers.