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<Paper uid="P06-1031">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics A Feedback-Augmented Method for Detecting Errors in the Writing of Learners of English</Title>
  <Section position="3" start_page="0" end_page="241" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> 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 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).</Paragraph>
    <Paragraph position="1"> 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.</Paragraph>
    <Paragraph position="2"> 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, rst, 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.</Paragraph>
    <Paragraph position="3"> 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.</Paragraph>
    <Paragraph position="4"> In order to reduce the drawback, the proposed method is augmented by feedback; it takes as feed-back 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 feed-back corpus is normally far smaller than that of a general corpus. To make it practical, this paper discusses the problem and explores its solution.</Paragraph>
    <Paragraph position="5"> The rest of this paper is structured as follows.</Paragraph>
    <Paragraph position="6"> Section 2 describes the method for detecting the target errors based on the mass count distinction.</Paragraph>
    <Paragraph position="7"> Section 3 explains how the method is augmented by feedback. Section 4 discusses experiments conducted to evaluate the proposed method.</Paragraph>
  </Section>
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