Paper: Understanding Differences in Perceived Peer-Review Helpfulness using Natural Language Processing

ACL ID W11-1402
Title Understanding Differences in Perceived Peer-Review Helpfulness using Natural Language Processing
Venue Innovative Use of NLP for Building Educational Applications
Session
Year 2011
Authors

Identifying peer-review helpfulness is an im- portant task for improving the quality of feed- back received by students, as well as for help- ing students write better reviews. As we tailor standard product review analysis techniques to our peer-review domain, we notice that peer- review helpfulness differs not only between students and experts but also between types of experts. In this paper, we investigate how different types of perceived helpfulness might influence the utility of features for automatic prediction. Our feature selection results show that certain low-level linguistic features are more useful for predicting student perceived helpfulness, while high-level cognitive con- structs are more effective in modeling experts’ perceived helpfulness.