UNT-Yahoo: SuperSenseLearner: combining SenseLearner with supersense and other coarse semantic features

Authors:
Rada Mihalcea;Andras Csomai;Massimiliano Ciaramita
Affiliations:
University of North Texas;University of North Texas;Yahoo! Research Barcelona
Venue:
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Year:
2007

Citing 7
Cited 0

WordNet: a lexical database for English

Communications of the ACM
Instance based learning with automatic feature selection applied to word sense disambiguation

COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A semantic concordance

HLT '93 Proceedings of the workshop on Human Language Technology
Evaluating the results of a memory-based word-expert approach to unrestricted word sense disambiguation

WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms

EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
SenseLearner: word sense disambiguation for all words in unrestricted text

ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
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

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe the SuperSenseLearner system that participated in the English all-words disambiguation task. The system relies on automatically-learned semantic models using collocational features coupled with features extracted from the annotations of coarse-grained semantic categories generated by an HMM tagger.