Automatic labeling of semantic roles
Computational Linguistics
Empirical acquisition of conceptual distinctions via dictionary definitions
Empirical acquisition of conceptual distinctions via dictionary definitions
The Notion of Argument in Prepositional Phrase Attachment
Computational Linguistics
Coverage and inheritance in the preposition project
Prepositions '06 Proceedings of the Third ACL-SIGSEM Workshop on Prepositions
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Semantic role labeling: an introduction to the special issue
Computational Linguistics
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
A corpus-based analysis of argument realization by preposition structures
Natural Language Engineering
Prepositions in applications: A survey and introduction to the special issue
Computational Linguistics
Exploiting semantic role resources for preposition disambiguation
Computational Linguistics
Disambiguation of preposition sense using linguistically motivated features
SRWS '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium
IRST-BP: preposition disambiguation based on chain clarifying relationships contexts
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
KU: word sense disambiguation by substitution
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Web-scale N-gram models for lexical disambiguation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Joint learning of preposition senses and semantic roles of prepositional phrases
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
What's in a preposition?: dimensions of sense disambiguation for an interesting word class
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Models and training for unsupervised preposition sense disambiguation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Spatial role labeling: Towards extraction of spatial relations from natural language
ACM Transactions on Speech and Language Processing (TSLP)
A joint model for extended semantic role labeling
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A fast, accurate, non-projective, semantically-enriched parser
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Using frames to disambiguate prepositions
Expert Systems with Applications: An International Journal
Exploiting partial annotations with EM training
WILS '12 Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure
Extracting Spatial Information From Place Descriptions
Proceedings of The First ACM SIGSPATIAL International Workshop on Computational Models of Place
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The SemEval-2007 task to disambiguate prepositions was designed as a lexical sample task. A set of over 25,000 instances was developed, covering 34 of the most frequent English prepositions, with two-thirds of the instances for training and one-third as the test set. Each instance identified a preposition to be tagged in a full sentence taken from the FrameNet corpus (mostly from the British National Corpus). Definitions from the Oxford Dictionary of English formed the sense inventories. Three teams participated, with all achieving supervised results significantly better than baselines, with a high fine-grained precision of 0.693. This level is somewhat similar to results on lexical sample tasks with open class words, indicating that significant progress has been made. The data generated in the task provides ample opportunitites for further investigations of preposition behavior.