A Statistical Model Based on the Three Head Words for Detecting Article Errors

Ryo NAGATA
Tatsuya IGUCHI
Fumito MASUI
Atsuo KAWAI
Naoki ISU

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E88-D    No.7    pp.1700-1706
Publication Date: 2005/07/01
Online ISSN: 
DOI: 10.1093/ietisy/e88-d.7.1700
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Educational Technology
Keyword: 
article errors,  Japanese learners of English,  three head words,  statistical model,  the data sparseness problem,  

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Summary: 
In this paper, we propose a statistical model for detecting article errors, which Japanese learners of English often make in English writing. It is based on the three head words--the verb head, the preposition, and the noun head. To overcome the data sparseness problem, we apply the backed-off estimate to it. Experiments show that its performance (F-measure=0.70) is better than that of other methods. Apart from the performance, it has two advantages: (i) Rules for detecting article errors are automatically generated as conditional probabilities once a corpus is given; (ii) Its recall and precision rates are adjustable.

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