Title :
Finding the WRITE stuff: automatic identification of discourse structure in student essays
Author :
Burstein, Jill ; Marcu, Daniel ; Knight, Kevin
Author_Institution :
ETS Technol., Princeton, NJ, USA
Abstract :
An essay-based discourse analysis system can help students improve their writing by identifying relevant essay-based discourse elements in their essays. Our discourse analysis software, which is embedded in Criterion, an online essay evaluation application, uses machine learning to identify discourse elements in student essays. The system makes decisions that exemplify how teachers perform this task. For instance, when grading student essays, teachers comment on the discourse structure. Teachers might explicitly state that the essay lacks a thesis statement or that an essay´s single main idea has insufficient support. Training the systems to model this behavior requires human judges to annotate a data sample of student essays. The annotation schema reflects the highly structured discourse of genres such as persuasive writing. Our discourse analysis system uses a voting algorithm that takes into account the discourse labeling decisions of three independent systems.
Keywords :
intelligent tutoring systems; learning (artificial intelligence); linguistics; natural languages; Criterion; annotation schema; essay-based discourse analysis system; intelligent tutor; machine learning; online essay evaluation application; student essays; voting algorithm; writing; Algorithm design and analysis; Application software; Feedback; Humans; Large-scale systems; Machine learning; Protocols; Testing; Voting; Writing;
Journal_Title :
Intelligent Systems, IEEE
DOI :
10.1109/MIS.2003.1179191