ABSTRACT

We propose that machine-learned computational models (MLCMs), in which the model parameters and perhaps even structure are learned from data, can complement extant approaches to the study of text and discourse. Such models are particularly useful when theoretical understanding is insufficient, when the data are rife with nonlinearities and interactivity, and when researchers aspire to take advantage of “big data.” Being fully instantiated computer programs, MLCMs can also be used for autonomous assessment and real-time intervention. We illustrate these ideas in the context of an eye movement–based MLCM of textbase comprehension during reading along connected text. Using a dataset where 104 participants read a 6,500-word text, we trained Random Forests models to predict comprehension scores from six eye movement features. The models were highly accurate (area under the receiver operating characteristic curve = .902; r = .661), robust, and generalized across participants, suggesting possible use in future studies. We conclude by arguing for an increased role of MLCMs in the future of discourse research.

摘要

我们提出，机器学习的计算模型（MLCM）可以补充现有的方法来研究文本和语篇，在该模型中，可以从数据中学习模型参数甚至结构。当理论上的理解不足，数据中充满非线性和交互性以及研究人员渴望利用“大数据”时，此类模型特别有用。作为完全实例化的计算机程序，MLCM还可以用于自主评估和实时干预。我们在沿连接的文本阅读过程中基于眼动的MLML理解文本库理解了这些思想。使用104位参与者阅读6,500字的文本的数据集，我们训练了Random Forests模型来预测六个眼动特征的理解分数。r = .661），稳健且在参与者中广泛使用，表明可能在未来的研究中使用。最后，我们争论说，MLCM在话语研究的未来中将发挥更大的作用。