Feature-rich error detection in scientific writing using logistic regression

Abstract

The goal of the Automatic Evaluation of Scientific Writing (AESW) Shared Task 2016 is to identify sentences in scientific articles which need editing to improve their correctness and readability or to make them better fit within the genre at hand. We encode many different types of errors occurring in the dataset by linguistic features. We use logistic regression to assign a probability indicating whether a sentence needs to be edited. We participate in both tracks at AESW 2016: Binary prediction and probabilistic estimation. In the former track, our model (HITS) gets the fifth place and in the latter one, it ranks first according to the evaluation metric.