爱沙尼亚语
计算机科学
判别式
背景(考古学)
人工智能
自然语言处理
多元统计
逻辑回归
机器学习
语言学
生物
哲学
古生物学
标识
DOI:10.1515/cllt-2016-0010
摘要
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.
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