Finding the WRITE Stuff: Automatic Identification of Discourse Structure in Student Essays
IEEE Intelligent Systems
An unsupervised method for detecting grammatical errors
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
A classifier-based approach to preposition and determiner error correction in L2 English
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Text-to-text semantic similarity for automatic short answer grading
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
A human-computer collaboration approach to improve accuracy of an automated English scoring system
IUNLPBEA '10 Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications
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This paper presents an automated scoring system which grades students' English writing tests. The system provides a score and diagnostic feedback to students without human's efforts. Target users are Korean students in junior high schools who learn English as a second language. The system takes a single English sentence as its input. Dealing with a single sentence as an input has some advantages on comparing the input with the answers given by human teachers and giving detailed feedback to the students. The system was developed and tested with the real test data collected through English tests given to third grade students in junior high school. Scoring requires two steps of the process. The first process is analyzing the input sentence in order to detect possible errors, such as spelling errors and syntactic errors. The second process is comparing the input sentence with given answers to identify the differences as errors. To evaluate the performance of the system, the output produced by the system is compared with the result provided by human raters. The score agreement value between a human rater and the system is quite close to the value between two human raters.