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Correcting Verb Selection Errors for ESL with the Perceptron

  • Xiaohua Liu
  • Bo Han
  • Ming Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6609)

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.

Keywords

verb selection perceptron learning ESL 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Xiaohua Liu
    • 1
    • 3
  • Bo Han
    • 2
  • Ming Zhou
    • 3
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.Department of Computer Science and Software EngineeringThe University of MelbourneVictoriaAustralia
  3. 3.Microsoft Research AsiaBeijingChina

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