tailieunhanh - Báo cáo khoa học: "A Feedback-Augmented Method for Detecting Errors in the Writing of Learners of English"

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 and a precision of and outperforms other methods used for comparison when augmented by feedback. . | A Feedback-Augmented Method for Detecting Errors in the Writing of Learners of English Ryo Nagata Hyogo University of Teacher Education 6731494 Japan rnagata@ Koichiro Morihiro Hyogo University of Teacher Education 6731494 Japan mori@ Abstract 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 and a precision of and outperforms other methods 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 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 Atsuo Kawai Mie University 5148507 Japan kawai@ Naoki Isu Mie University 5148507 Japan isu@ Japanese language does not have a mass count distinction system similar to that of English. Thus it is favorable for error detection

• Báo cáo khoa học
• A Feedback Augmented Method
• Detecting Errors in the Writing
• Learners of English
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Bài giảng Xử lý ngôn ngữ tự nhiên
• Ngôn ngữ tự nhiên
• Kỹ thuật lập trình
• Xử lý nhập nhằng ngữ nghĩa
• Xử lý ngữ nghĩa
• Xử lý ngôn ngữ
• Natural Language Processing
• Ngôn ngữ lập trình
• Nhập nhằng cấu trúc
• Tri thức về ngôn ngữ
• Mô hình n gram
• Dịch máy
• Phương pháp dịch máy
• Hiểu ngôn ngữ
• Phân tích ngữ nghĩa
• Phân loại tin tự động
• Biểu diễn vị từ
• Thuộc tính về sự kiện
• Thuộc tính về sự kiện
• Mô hình ngôn ngữ Google Book N grams
• Mô hình ngôn ngữ KenLM
• Phương pháp làm mịn
• Tách từ tiếng Việt
• Gán nhãn từ loại
• Phân tích cú pháp
• Nghĩa từ vựng
• Phân giải nhập nhằng từ
• Phân loại văn bản
• Lexical chain
• Phản hồi thông tin
• Bài toán PTCP
• Cấu trúc ngữ pháp
• Phân tích cú pháp xác suất
• CKY kết hợp xác suất
• Văn phạm phi ngữ cảnh xác suất
• Hình thái học