A maximum entropy approach to natural language processing
Computational Linguistics
Presumptive Meanings: The Theory of Generalized Conversational Implicature
Presumptive Meanings: The Theory of Generalized Conversational Implicature
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Optimization, maxent models, and conditional estimation without magic
NAACL-Tutorials '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Tutorials - Volume 5
A factuality profiler for eventualities in text
A factuality profiler for eventualities in text
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
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
A metalearning approach to processing the scope of negation
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Local textual inference: can it be defined or circumscribed?
EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
Committed belief annotation and tagging
ACL-IJCNLP '09 Proceedings of the Third Linguistic Annotation Workshop
Proceedings of the Workshop on Negation and Speculation in Natural Language Processing
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
Automatic committed belief tagging
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
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Natural language understanding depends heavily on assessing veridicality-whether events mentioned in a text are viewed as happening or not-but little consideration is given to this property in current relation and event extraction systems. Furthermore, the work that has been done has generally assumed that veridicality can be captured by lexical semantic properties whereas we show that context and world knowledge play a significant role in shaping veridicality. We extend the FactBank corpus, which contains semantically driven veridicality annotations, with pragmatically informed ones. Our annotations are more complex than the lexical assumption predicts but systematic enough to be included in computational work on textual understanding. They also indicate that veridicality judgments are not always categorical, and should therefore be modeled as distributions. We build a classifier to automatically assign event veridicality distributions based on our new annotations. The classifier relies not only on lexical features like hedges or negations, but also on structural features and approximations of world knowledge, thereby providing a nuanced picture of the diverse factors that shape veridicality. "All I know is what I read in the papers" -Will Rogers