Generation from lexical conceptual structures
NAACL-ANLP-Interlinguas '00 Proceedings of the 2000 NAACL-ANLP Workshop on Applied interlinguas: practical applications of interlingual approaches to NLP - Volume 2
Metonymy resolution as a classification task
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
SemEval-2007 task 04: classification of semantic relations between nominals
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
SemEval-2007 task 08: metonymy resolution at SemEval-2007
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
On the semantics of noun compounds
Computer Speech and Language
Semantic role labeling using complete syntactic analysis
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Automatic interpretation of loosely encoded input
Artificial Intelligence
Discovery and evaluation of non-taxonomic relations in domain ontologies
International Journal of Metadata, Semantics and Ontologies
Combining collocations, lexical and encyclopedic knowledge for metonymy resolution
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Refining non-taxonomic relation labels with external structured data to support ontology learning
Data & Knowledge Engineering
A knowledge-rich approach to identifying semantic relations between nominals
Information Processing and Management: an International Journal
Local and global context for supervised and unsupervised metonymy resolution
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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In this paper we present a semantic architecture that was employed for processing two different SemEval 2007 tasks: Task 4 (Classification of Semantic Relations between Nominals) and Task 8 (Metonymy Resolution). The architecture uses multiple forms of syntactic, lexical, and semantic information to inform a classification-based approach that generates a different model for each machine learning algorithm that implements the classification. We used decision trees, decision rules, logistic regression and lazy classifiers. A voting module selects the best performing module for each task evaluated in SemEval 2007. The paper details the results obtained when using the semantic architecture.