A computational approach to detecting collocation errors in the writing of non-native speakers of English

Abstract:

This paper describes the first prototype of an automated tool for detecting collocation errors in texts written by non-native speakers of English. Candidate strings are extracted by pattern matching over POS-tagged text. Since learner texts often contain spelling and morphological errors, the tool attempts to automatically correct them in order to reduce noise. For a measure of collocation strength, we use the rank-ratio statistic calculated over one billion words of native-speaker texts. Two human annotators evaluated the system's performance. We report the overall results, as well as detailed error analyses, and discuss possible improvements for the future.

Keywords: collocation; automatic error detection; learner texts; ESL; natural language processing; second language learning; computer-assisted language learning; annotation; m

Document Type: Research article

DOI: 10.1080/09588220802343561

Affiliations: 1: R&D, Educational Testing Service, Princeton, USA 2: Department of Psychology, Hunter College of CUNY, New York, USA