Detecting article errors based on the mass count distinction

  • Authors:

  • Ryo Nagata;Takahiro Wakana;Fumito Masui;Atsuo Kawai;Naoki Isu

  • Affiliations:

  • Hyogo University of Teacher Education, Yashiro, Japan;Mie University, Tsu, Japan;Mie University, Tsu, Japan;Mie University, Tsu, Japan;Mie University, Tsu, Japan

  • Venue:
  • IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
  • Year:
  • 2005

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Abstract

This paper proposes a method for detecting errors concerning article usage and singular/plural usage based on the mass count distinction. Although the mass count distinction is particularly important in detecting these errors, it has been pointed out that it is hard to make heuristic rules for distinguishing mass and count nouns. To solve the problem, first, instances of mass and count nouns are automatically collected from a corpus exploiting surface information in the proposed method. Then, words surrounding the mass (count) instances are weighted based on their frequencies. Finally, the weighted words are used for distinguishing mass and count nouns. After distinguishing mass and count nouns, the above errors can be detected by some heuristic rules. Experiments show that the proposed method distinguishes mass and count nouns in the writing of Japanese learners of English with an accuracy of 93% and that 65% of article errors are detected with a precision of 70%.