The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluation of text coherence for electronic essay scoring systems
Natural Language Engineering
Automated scoring using a hybrid feature identification technique
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Automated rating of ESL essays
HLT-NAACL-EDUC '03 Proceedings of the HLT-NAACL 03 workshop on Building educational applications using natural language processing - Volume 2
The second release of the RASP system
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
The Knowledge Engineering Review
An Unsupervised Automated Essay Scoring System
IEEE Intelligent Systems
Correction detection and error type selection as an ESL educational aid
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
EACL 2012 Proceedings of the EACL 2012 Joint Workshop of LINGVIS & UNCLH
Modeling coherence in ESOL learner texts
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
HOO 2012: a report on the preposition and determiner error correction shared task
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Predicting learner levels for online exercises of Hebrew
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Informing determiner and preposition error correction with word clusters
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
NUS at the HOO 2012 shared task
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
HOO 2012 error recognition and correction shared task: Cambridge University submission report
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Korea University system in the HOO 2012 shared task
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
The UI system in the HOO 2012 shared task on error correction
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
A meta learning approach to grammatical error correction
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
On second language tutoring through womb grammars
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Scaling short-answer grading by combining peer assessment with algorithmic scoring
Proceedings of the first ACM conference on Learning @ scale conference
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We demonstrate how supervised discriminative machine learning techniques can be used to automate the assessment of 'English as a Second or Other Language' (ESOL) examination scripts. In particular, we use rank preference learning to explicitly model the grade relationships between scripts. A number of different features are extracted and ablation tests are used to investigate their contribution to overall performance. A comparison between regression and rank preference models further supports our method. Experimental results on the first publically available dataset show that our system can achieve levels of performance close to the upper bound for the task, as defined by the agreement between human examiners on the same corpus. Finally, using a set of 'outlier' texts, we test the validity of our model and identify cases where the model's scores diverge from that of a human examiner.