Making large-scale support vector machine learning practical
Advances in kernel methods
The automated acquisition of topic signatures for text summarization
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Modeling and Predicting the Helpfulness of Online Reviews
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
How opinions are received by online communities: a case study on amazon.com helpfulness votes
Proceedings of the 18th international conference on World wide web
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
NLPIR4DL '09 Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries
IEEE Transactions on Knowledge and Data Engineering
Identifying problem localization in peer-review feedback
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Understanding differences in perceived peer-review helpfulness using natural language processing
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
An interactive analytic tool for peer-review exploration
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
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Identifying peer-review helpfulness is an important task for improving the quality of feedback 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 incorporating additional specialized features tailored 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 specific auxiliary features can further improve helpfulness prediction.