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Abstract
GAMBL is a word expert approach to WSD in which each word expert is trained using memory based learning. Joint feature selection and algorithm parameter optimization are achieved with a genetic algorithm (GA). We use a cascaded classifier approach in which the GA optimizes local context features and the output of a separate keyword classifier (rather than also optimizing the keyword features together with the local context features). A further innovation on earlier versions of memory based WSD is the use of grammatical relation and chunk features. This paper presents the architecture of the system briefly, and discusses its performance on the English lexical sample and all words tasks in SENSEVAL-3.

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MLA
Decadt, Bart, Veronique Hoste, Walter Daelemans, et al. “GAMBL, Genetic Algorithm Optimization of Memory-based WSD.” Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text. Association for Computational Linguistics, 2004. 108–112. Print.
APA
Decadt, B., Hoste, V., Daelemans, W., & van den Bosch, A. (2004). GAMBL, genetic algorithm optimization of memory-based WSD. Third international workshop on the evaluation of systems for the semantic analysis of text (pp. 108–112). Presented at the 3rd International workshop on the Evaluation of Systems for the Semantic Analysis of Text (SENSEVAL-3) ; held in conjunction with the 42nd Annual meeting of the Association for Computational Linguistics (ACL 2004), Association for Computational Linguistics.
Chicago author-date
Decadt, Bart, Veronique Hoste, Walter Daelemans, and Antal van den Bosch. 2004. “GAMBL, Genetic Algorithm Optimization of Memory-based WSD.” In Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, 108–112. Association for Computational Linguistics.
Chicago author-date (all authors)
Decadt, Bart, Veronique Hoste, Walter Daelemans, and Antal van den Bosch. 2004. “GAMBL, Genetic Algorithm Optimization of Memory-based WSD.” In Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, 108–112. Association for Computational Linguistics.
Vancouver
1.
Decadt B, Hoste V, Daelemans W, van den Bosch A. GAMBL, genetic algorithm optimization of memory-based WSD. Third international workshop on the evaluation of systems for the semantic analysis of text. Association for Computational Linguistics; 2004. p. 108–12.
IEEE
[1]
B. Decadt, V. Hoste, W. Daelemans, and A. van den Bosch, “GAMBL, genetic algorithm optimization of memory-based WSD,” in Third international workshop on the evaluation of systems for the semantic analysis of text, Barcelona, Spain, 2004, pp. 108–112.
@inproceedings{598103,
  abstract     = {GAMBL is a word expert approach to WSD in which each word expert is trained using memory based learning. Joint feature selection and algorithm parameter optimization are achieved with a genetic algorithm (GA). We use a cascaded classifier approach in which the GA optimizes local context features and the output of a separate keyword classifier (rather than also optimizing the keyword features together with the local context features). A further innovation on earlier versions of memory based WSD is the use of grammatical relation and chunk features. This paper presents the architecture of the system briefly, and discusses its performance on the English lexical sample and all words tasks in SENSEVAL-3.},
  author       = {Decadt, Bart and Hoste, Veronique and Daelemans, Walter and van den Bosch, Antal},
  booktitle    = {Third international workshop on the evaluation of systems for the semantic analysis of text},
  language     = {eng},
  location     = {Barcelona, Spain},
  pages        = {108--112},
  publisher    = {Association for Computational Linguistics},
  title        = {GAMBL, genetic algorithm optimization of memory-based WSD},
  year         = {2004},
}