Everyday people produce written language in an effort to communicate with one another. Appropriately, numerous studies have sought to measure this effort in order to better understand the process of writing. However, few studies have investigated approaches to gauge the effort of a given text automatically. This paper aims to bridge that gap by working towards an automatic measure of effort in writing. This thesis achieves this goal by motivating such a measure, reviewing relevant prior work, offering a clear definition of effort based on prior research, procuring a labeled dataset, and conducting experiments via supervised learning regression techniques. Overall, this thesis develops a model that attains favorable results on an unseen test set.
|Commitee:||Anderson, Kenneth, Foltz, Peter|
|School:||University of Colorado at Boulder|
|School Location:||United States -- Colorado|
|Source:||MAI 58/06M(E), Masters Abstracts International|
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