Creating a manually error-tagged and shallow-parsed learner corpus

Ryo Nagata1,  Edward Whittaker2,  Vera Sheinman2
1Konan University, 2JIEM


Abstract

The availability of learner corpora, especially those which have been manually error-tagged or shallow-parsed, is still limited. This means that researchers do not have a common development and test set for natural language processing of learner English such as for grammatical error detection. Given this background, we created a novel learner corpus that was manually error-tagged and shallow parsed. This corpus is available for research and educational purposes on the web. In this paper, we describe it in detail together with its data-collection method and annotation schemes. Another contribution of this paper is that we take the first step toward evaluating the performance of existing POStagging/chunking techniques on learner corpora using the created corpus. These contributions will facilitate further research in related areas such as grammatical error detection and automated essay scoring.




Full paper: http://www.aclweb.org/anthology/P/P11/P11-1121.pdf