Paragraph Clustering for Intrinsic Plagiarism Detection using a Stylistic Vector Space Model with Extrinsic Features

Our approach to the task of intrinsic plagiarism detection uses a vectorspace model which eschews surface features in favor of richer extrinsic features, including those based on latent semantic analysis in a larger external corpus. We posit that the popularity and success of surface n-gram features is mostly due to the topic-biased nature of current artificial evaluations, a problem which unfortunately extends to the present PAN evaluation. One interesting of aspect of our approach is our way of dealing with small, imbalanced span sizes; we improved performance considerably in our development evaluation by countering these effect using the expected difference of sums of random variables.