Correcting Verb Selection Errors for ESL with the Perceptron

Abstract

We study the task of correcting verb selection errors for English as a Second Language (ESL) learners, which is meaningful but also challenging. The difficulties of this task lie in two aspects: the lack of annotated data and the diversity of verb usage context. We propose a perceptron based novel approach to this task. More specifically, our method generates correction candidates using predefined confusion sets, to avoid the tedious and prohibitively unaffordable human labeling; moreover, rich linguistic features are integrated to represent verb usage context, using a global linear model learnt by the perceptron algorithm. The features used in our method include a language model, local text, chunks, and semantic collocations. Our method is evaluated on both synthetic and real-world corpora, and consistently achieves encouraging results, outperforming all baselines.