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A Feedback-Augmented Method for Detecting Errors in the Writing of Learners of English. Ryo Nagata et al. Hyogo University of Teacher Education ACL 2006. Objective. Detect singular-plural errors in English writing I ate a lot of chicken . I ate a lot of chickens. Approach.
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A Feedback-Augmented Method for Detecting Errors in the Writing of Learners of English Ryo Nagata et al. Hyogo University of Teacher Education ACL 2006
Objective • Detect singular-plural errors in English writing • I ate a lot of chicken. • I ate a lot of chickens.
Approach • Learn a decision list to separate mass nouns from count nouns • The paper is made of hemp pulp. • I read the paper. • Check if the target noun has the correct form • singular or plural
Decision List Training Corpus • British National Corpus • EDR Corpus • Instance format • She ate fried chicken/mass for dinner • Feature • Noun phrase components (e.g. fried) • Context words (e.g. she, ate, for, dinner) • Sample decision rules • eat-3 mass • frynp mass • for+3 mass • dinner+3 mass
Ranking Decision Rules • Rank by log-likelihood ratio • Example
Decision List Feedback Training Corpus • Use marked essays by English learners • More domain-specific • Three ways to use • Add into BNC and EDR corpora • Feedback corpus too small to affect p(MC|wc) • Increase weight of feedback corpus • Increase weight of feedback corpus even more
Increasing the Weight of Feedback Corpus • Increase the weight of feedback corpus by statistical confidence
Increasing the Weight of Feedback Corpus Even More • Take the log of the general corpus’ confidence
Error Detection • Use decision list to determine whether a noun is mass or count • Step 1: Mass noun in plural form error • Step 2:
Error Detection (Cont.) • Step 3:
Testing Corpus • 47 essays by Japanese English learners • 105 errors identified by professional English marker
Experiment Result • DL: decision list • FB: add directly • fb1: increase weight by confidence • fb2: increase weight more
Conclusion • Decision list better than rule-based and web-based methods • Feedback corpus better than general corpus only