A Feedback-Augmented Method for Detecting Errors in the Writing of Learners of English

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 241–248, Sydney, July 2006. c⃝2006 Association for Computational Linguistics A Feedback-Augmented Method for Detecting Errors in the Writing of Learners of English Ryo Nagata Hyogo University of Teacher Education 6731494, Japan rnagata@hyogo-u.ac.jp Atsuo Kawai Mie University 5148507, Japan kawai@ai.info.mie-u.ac.jp Koichiro Morihiro Hyogo University of Teacher Education 6731494, Japan mori@hyogo-u.ac.jp Naoki Isu Mie University 5148507, Japan isu@ai.info.mie-u.ac.jp Abstract This paper proposes a method for detect- ing errors in article usage and singular plu- ral usage based on the mass count distinc- tion. First, it learns decision lists from training data generated automatically to distinguish mass and count nouns. Then, in order to improve its performance, it is augmented by feedback that is obtained from the writing of learners. Finally, it de- tects errors by applying rules to the mass count distinction. Experiments show that it achieves a recall of 0.71 and a preci- sion of 0.72 and outperforms other meth- ods used for comparison when augmented by feedback. 1 Introduction Although several researchers (Kawai et al., 1984; McCoy et al., 1996; Schneider and McCoy, 1998; Tschichold et al., 1997) have shown that rule- based methods are effective to detecting gram- matical errors in the writing of learners of En- glish, it has been pointed out that it is hard to write rules for detecting errors concerning the ar- ticles and singular plural usage. To be precise, it is hard to write rules for distinguishing mass and count nouns which are particularly important in detecting these errors (Kawai et al., 1984). The major reason for this is that whether a noun is a mass noun or a count noun greatly depends on its meaning or its surrounding context (refer to Al- lan (1980) and Bond (2005) for details of the mass count distinction). The above errors are very common among Japanese learners of English (Kawai et al., 1984; Izumi et al., 2003). This is perhaps because the Japanese language does not have a mass count dis- tinction system similar to that of English. Thus, it is favorable for error detection systems aiming at Japanese learners to be capable of detecting these errors. In other words, such systems need to some- how distinguish mass and count nouns. This paper proposes a method for distinguishing mass and count nouns in context to complement the conventional rules for detecting grammatical errors. In this method, first, training data, which consist of instances of mass and count nouns, are automatically generated from a corpus. Then, decision lists for distinguishing mass and count nouns are learned from the training data. Finally, the decision lists are used with the conventional rules to detect the target errors. The proposed method requires a corpus to learn decision lists for distinguishing mass and count nouns. General corpora such as newspaper ar- ticles can be used for the purpose. However, a drawback to it is that there are differences in character between general corpora and the …

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