PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

GAMBL, Genetic Algorithm Optimization of Memory-Based WSD
Bart Decadt, Véronique Hoste, Walter Daelemans and Antal Van den Bosch
In: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (Senseval-3), Barcelona, Spain(2004).

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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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:363
Deposited By:Walter Daelemans
Deposited On:17 December 2004