Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
The rhetorical parsing of natural language texts
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Automated scoring using a hybrid feature identification technique
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Comlex Syntax: building a computational lexicon
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Automatic essay grading with probabilistic latent semantic analysis
EdAppsNLP 05 Proceedings of the second workshop on Building Educational Applications Using NLP
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E-rater is an operational automated essay scoring application. The system combines several NLP tools that identify linguistic features in essays for the purpose of evaluating the quality of essay text. The application currently identifies a variety of syntactic, discourse, and topical analysis features. We have maintained two clear visions of e-rater's development. First, new linguistically-based features would be added to strengthen connections between human scoring guide criteria and e-rater scores. Secondly, e-rater would be adapted to automatically provide explanatory feedback about writing quality. This paper provides two examples of the flexibility of e-rater's modular architecture for continued application development toward these goals. Specifically, we discuss a) how additional features from rhetorical parse trees were integrated into e-rater, and b) how the salience of automatically generated discourse-based essay summaries was evaluated for use as instructional feedback through the re-use of e-rater's topical analysis module.