Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Identifying off-topic student essays without topic-specific training data
Natural Language Engineering
Educational Software Features that Encourage and Discourage “Gaming the System”
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Measuring the use of factual information in test-taker essays
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
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Our work addresses the problem of predicting whether an essay is off-topic to a given prompt or question without any previously-seen essays as training data. Prior work has used similarity between essay vocabulary and prompt words to estimate the degree of ontopic content. In our corpus of opinion essays, prompts are very short, and using similarity with such prompts to detect off-topic essays yields error rates of about 10%. We propose two methods to enable better comparison of prompt and essay text. We automatically expand short prompts before comparison, with words likely to appear in an essay to that prompt. We also apply spelling correction to the essay texts. Both methods reduce the error rates during off-topic essay detection and turn out to be complementary, leading to even better performance when used in unison.