Automatic article restoration

John Lee*

*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

27 Citations (Scopus)

Abstract

One common mistake made by non-native speakers of English is to drop the articles a, an, or the. We apply the log-linear model to automatically restore missing articles based on features of the noun phrase. We first show that the model yields competitive results in article generation. Further, we describe methods to adjust the model with respect to the initial quality of the sentence. Our best results are 20.5% article error rate (insertions, deletions and substitutions) for sentences where 30% of the articles have been dropped, and 38.5% for those where 70% of the articles have been dropped.
Original languageEnglish
Title of host publicationHLT-NAACL 2004 - Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Student Research Workshop
PublisherAssociation for Computational Linguistics
Pages31-36
DOIs
Publication statusPublished - 2004
Externally publishedYes
Event2004 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics - Student Research Workshop, HLT-NAACL 2004 - Boston, United States
Duration: 2 May 20047 May 2004
https://aclanthology.org/N04-2000/

Publication series

NameHLT-NAACL 2004 - Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Student Research Workshop

Conference

Conference2004 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics - Student Research Workshop, HLT-NAACL 2004
Country/TerritoryUnited States
CityBoston
Period2/05/047/05/04
Internet address

Bibliographical note

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