This paper proposes a method for detecting errors in article usage and singular plural usage based on the mass count distinction . 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 detects errors by applying rules to the mass count distinction . Experiments show that it achieves a recall of 0.71 and a precision of 0.72 and outperforms other methods used for comparison when augmented by feedback . Although several researchers ( Kawai et al . , 1984 ; McCoy et al . , 1996 ; Schneider and McCoy , 1998 ; Tschichold et al . , 1997 ) have shown that rulebased methods are effective to detecting grammatical errors in the writing of learners of English , it has been pointed out that it is hard to write rules for detecting errors concerning the articles 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 Allan ( 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 distinction 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 somehow 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 articles can be used for the purpose . However , a drawback to it is that there are differences in character between general corpora and the writing of non-native learners of English ( Granger , 1998 ; Chodorow and Leacock , 2000 ) . For instance , Chodorow and Leacock ( 2000 ) point out that the word concentrate is usually used as a noun in a general corpus whereas it is a verb 91 % of the time in essays written by non-native learners of English . Consequently , the differences affect the performance of the proposed method . In order to reduce the drawback , the proposed method is augmented by feedback ; it takes as feedback learners ' essays whose errors are corrected by a teacher of English ( hereafter , referred to as the feedback corpus ) . In essence , the feedback corpus could be added to a general corpus to generate training data . Or , ideally training data could be generated only from the feedback corpus just as from a general corpus . However , this causes a serious problem in practice since the size of the feedback corpus is normally far smaller than that of a general corpus . To make it practical , this paper discusses the problem and explores its solution . The rest of this paper is structured as follows . Section 2 describes the method for detecting the target errors based on the mass count distinction . Section 3 explains how the method is augmented by feedback . Section 4 discusses experiments conducted to evaluate the proposed method . This paper has proposed a feedback-augmented method for distinguishing mass and count nouns to complement the conventional rules for detecting grammatical errors . The experiments have shown that the proposed method detected 71 % of the target errors in the writing of Japanese learners of English with a precision of 72 % when it was augmented by feedback . From the results , we conclude that the feedback-augmented method is effective to detecting errors concerning the articles and singular plural usage in the writing of Japanese learners of English . Although it is not taken into account in this paper , the feedback corpus contains further useful information . For example , we can obtain training data consisting of instances of errors by comparing the feedback corpus with its original corpus . Also , comparing it with the results of detection , we can know performance of each rule used in the detection , which make it possible to increase or decrease their log-likelihood ratios according to their performance . We will investigate how to exploit these sources of information in future work .

Challenge: Although they are common, writing rules for detecting errors concerning the articles and singular plural usage especially for distinguishing mass and count nouns is hard.
Approach: They propose to complement conventional rules with decision lists learned from automatically generated training data and feedback obtained from the writing of learners.
Outcome: The proposed method outperforms other methods by achieving a recall of 0.71 and a precision of 0.72 when augmented by feedback.