Statistical models of word-sense disambiguation are often based on a small number of contextual features or on a model that is assumed to characterize the interactions among a set of features. Model selection is presented as an alternative to these approaches, where a sequential search of possible models is conducted in order to find the model that best characterizes the interactions among features. This paper expands existing model selection methodology and presents the first comparative study of model selection search strategies and evaluation criteria when applied to the problem of building probabilistic classifiers for word-sense disambiguation.
In order to utilize models with more complicated interactions among feature variables, @cite_8 introduce the use of sequential model selection and decomposable models for word--sense disambiguation. They recommended a model selection procedure using BSS and the exact conditional test in combination with a test for model predictive power. In their procedure, the exact conditional test was used to guide the generation of new models and the test of model predictive power was used to select the final model from among those generated during the search.
@cite_8
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The use of automated subset search algorithms is reviewed and issues concerning model selection and selection criteria are discussed. In addition, a Monte Carlo study is reported which presents data regarding the frequency with which authentic and noise variables are selected by automated subset algorithms. In particular, the effects of the correlation between predictor variables, the number of candidate predictor variables, the size of the sample, and the level of significance for entry and deletion of variables were studied for three automated subset algorithms: BACKWARD ELIMINATION, FORWARD SELECTION, and STEPWISE. Results indicated that: (1) the degree of correlation between the predictor variables affected the frequency with which authentic predictor variables found their way into the final model; (2) the number of candidate predictor variables affected the number of noise variables that gained entry to the model; (3) the size of the sample was of little practical importance in determining the number of authentic variables contained in the final model; and (4) the population multiple coefficient of determination could be faithfully estimated by adopting a statistic that is adjusted by the total number of candidate predictor variables rather than the number of variables in the final model.