Detection and Correction of Preposition and Determiner Errors in English: HOO 2012
نویسندگان
چکیده
This paper reports on our work in the HOO 2012 shared task. The task is to automatically detect, recognize and correct the errors in the use of prepositions and determiners in a set of given test documents in English. For that, we have developed a hybrid system of an n-gram statistical model along with some rule-based techniques. The system has been trained on the HOO shared task’s training datasets and run on the test set given. We have submitted one run, which has demonstrated an F-score of 7.1, 6.46 and 2.58 for detection, recognition and correction respectively before revision and F-score of 8.22, 7.59 and 3.16 for detection, recognition and correction respectively after revision.
منابع مشابه
Informing Determiner and Preposition Error Correction with Word Clusters
We extend our n-gram-based data-driven prediction approach from the Helping Our Own (HOO) 2011 Shared Task (Boyd and Meurers, 2011) to identify determiner and preposition errors in non-native English essays from the Cambridge Learner Corpus FCE Dataset (Yannakoudakis et al., 2011) as part of the HOO 2012 Shared Task. Our system focuses on three error categories: missing determiner, incorrect de...
متن کاملInforming Determiner and Preposition Error Correction with Hierarchical Word Clustering
We extend our n-gram-based data-driven prediction approach from the Helping Our Own (HOO) 2011 Shared Task (Boyd and Meurers, 2011) to identify determiner and preposition errors in non-native English essays from the Cambridge Learner Corpus FCE Dataset (Yannakoudakis et al., 2011) as part of the HOO 2012 Shared Task. Our system focuses on three error categories: missing determiner, incorrect de...
متن کاملKU Leuven at HOO-2012: A Hybrid Approach to Detection and Correction of Determiner and Preposition Errors in Non-native English Text
In this paper we describe the technical implementation of our system that participated in the Helping Our Own 2012 Shared Task (HOO-2012). The system employs a number of preprocessing steps and machine learning classifiers for correction of determiner and preposition errors in non-native English texts. We use maximum entropy classifiers trained on the provided HOO-2012 development data and a la...
متن کاملNAIST at the HOO 2012 Shared Task
This paper describes the Nara Institute of Science and Technology (NAIST) error correction system in the Helping Our Own (HOO) 2012 Shared Task. Our system targets preposition and determiner errors with spelling correction as a pre-processing step. The result shows that spelling correction improves the Detection, Correction, and Recognition Fscores for preposition errors. With regard to preposi...
متن کاملA Naive Bayes classifier for automatic correction of preposition and determiner errors in ESL text
This is the report for the CNGL ILT3 team entry to the HOO shared task. A Naive-Bayes-based classifier was used in the task which involved error detection and correction in ESL exam scripts. Our system placed 11th out of 14 teams for the detection and recognition tasks and 11th out of 13 teams for the correction task on the based on f-score for both preposition and determiner errors.
متن کامل