Automated Suggestions for Miscollocations

Anne Li E. Liu, David Wible, Nai Lung Tsao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Scopus citations

Abstract

One of the most common and persistent error types in second language writing is collocation errors, such as learn knowledge instead of gain or acquire knowledge, or make damage rather than cause damage. In this work-inprogress report, we propose a probabilistic model for suggesting corrections to lexical collocation errors. The probabilistic model incorporates three features: word association strength (MI), semantic similarity (via Word- Net) and the notion of shared collocations (or intercollocability). The results suggest that the combination of all three features outperforms any single feature or any combination of two features.

Original languageEnglish
Title of host publicationProceedings of the 4th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2009
EditorsJill Burstein, Claudia Leacock, Joel Tetreault
PublisherAssociation for Computational Linguistics (ACL)
Pages47-50
Number of pages4
ISBN (Electronic)9781932432374
StatePublished - 2009
Event4th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2009 - Boulder, United States
Duration: 5 Jun 2009 → …

Publication series

NameProceedings of the 4th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2009

Conference

Conference4th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2009
Country/TerritoryUnited States
CityBoulder
Period5/06/09 → …

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