This paper explores a text classification problem we will call lect modeling, an example of what has been termed computational sociolinguistics.

Our results validate the treatment of lect modeling as a text classification problem — albeit a hard one — and constitute a case for future research in computational sociolinguistics.

Given, then, that there are distinct differences among what we term UpSpeak and DownSpeak, we treat Social Power Modeling as an instance of text classification (or categorization): we seek to assign a class (UpSpeak or DownSpeak) to a text sample.

Implicitly or explicitly, previous work has mostly treated automated assessment as a supervised text classification task, where training texts are labelled with a grade and unlabelled test texts are fitted to the same grade point scale via a regression step applied to the classifier output (see Section 6 for more details).

Discriminative classification techniques often outperform non-discriminative ones in the context of text classification (J oachims, 1998).

This system shows that treating AA as a text classification problem is viable, but the feature types are all fairly shallow, and the approach doesn’t make efficient use of the training data as a separate classifier is trained for each grade point.