Accessible Requires Authentication Published by De Gruyter Mouton December 7, 2017

Pitting corpus-based classification models against each other: a case study for predicting constructional choice in written Estonian

Jane Klavan

Abstract

In the context of constructional alternatives, we may assume that speakers’ choice between alternative forms is influenced by a multitude of factors. At the moment, multivariate statistical classification modelling seems to be the best tool available to capture this knowledge quantitatively. There is a vast array of techniques available. In this paper, two distinct modelling techniques are applied – logistic regression and naïve discriminative learning – to predict the choice between two constructional alternatives in written Estonian. One of the central questions in statistical modelling concerns the evaluation of model fit. It is proposed that for linguistic analysis, the performance of alternative corpus-based models can be evaluated by, first, pitting them against each other and second, pitting them against experimental data. Previous work on modelling constructional and lexical choice has focused on one of the two aspects. The present paper takes this line of analysis further by combining the two approaches.

Funding statement: This study has been supported by a research grant from the Estonian Research Council (PUT1358 “The Making and Breaking of Models: Experimentally Validating Classification Models in Linguistics”).

Acknowledgments

I am grateful to the three anonymous referees for the journal – a number of issues are hopefully clearer now. I am grateful to Piia Taremaa and Ann Veismann for their meticulous comments on an earlier version of this paper. Many thanks to Arvi Tavast for insightful discussions and for posing difficult questions. I am very much indebted to Dagmar Divjak for the continued cooperation and discussions on the overall subject matter. The views expressed and the responsibility for any errors remain my own.

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Published Online: 2017-12-07
Published in Print: 2020-10-25

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