Skip to main navigation Skip to search Skip to main content

Automatic classification of article errors in L2 written english

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper presents an approach to the automatic classification of article errors in non-native (L2) English writing, using data chosen from the MELD corpus that was purposely selected to contain only cases with article errors. We report on two experiments on the data: one to assess the performance of different machine learning algorithms in predicting correct article usage, and the other to determine the feasibility of using the MELD data to identify which linguistic properties of the noun phrase containing the article are the most salient with respect to the classification of errors in article usage.

Original languageEnglish
Title of host publicationProceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
PublisherAAAI press
Pages259-264
Number of pages6
ISBN (Print)9781577354475
StatePublished - 2010

Publication series

NameProceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

Fingerprint

Dive into the research topics of 'Automatic classification of article errors in L2 written english'. Together they form a unique fingerprint.

Cite this