Towards a standard upper ontology
Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
The EuroWordNet Base Concepts and Top Ontology
The EuroWordNet Base Concepts and Top Ontology
Supersense tagging of unknown nouns in WordNet
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Supersense tagging of unknown nouns using semantic similarity
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Broad-coverage sense disambiguation and information extraction with a supersense sequence tagger
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
International Journal of Web Engineering and Technology
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We present a corpus-based supervised learning system for coarse-grained sense disambiguation. In addition to usual features for training in word sense disambiguation, our system also uses Base Level Concepts automatically obtained from WordNet. Base Level Concepts are some synsets that generalize a hyponymy sub-hierarchy, and provides an extra level of abstraction as well as relevant information about the context of a word to be disambiguated. Our experiments proved that using this type of features results on a significant improvement of precision. Our system has achieved almost 0.8 F1 (fifth place) in the coarse--grained English all-words task using a very simple set of features plus Base Level Concepts annotation.