Paper: Automatically Predicting Peer-Review Helpfulness

ACL ID P11-2088
Title Automatically Predicting Peer-Review Helpfulness
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

Identifying peer-review helpfulness is an im- portant task for improving the quality of feed- back that students receive from their peers. As a first step towards enhancing existing peer- review systems with new functionality based on helpfulness detection, we examine whether standard product review analysis techniques also apply to our new context of peer reviews. In addition, we investigate the utility of in- corporating additional specialized features tai- lored to peer review. Our preliminary results show that the structural features, review uni- grams and meta-data combined are useful in modeling the helpfulness of both peer reviews and product reviews, while peer-review spe- cific auxiliary features can further improve helpfulness prediction.