A Feedback-Augmented Method for Detecting Errors in the Writing

of Learners of EnglishRyo 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 mor

e

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