NDLI: Sentence Correction Incorporating Relative Position and Parse Template Language Models

Content Provider	IEEE Xplore Digital Library
Author	Chung-Hsien Wu Chao-Hong Liu Harris, M. Liang-Chih Yu
Copyright Year	2006
Abstract	Sentence correction has been an important emerging issue in computer-assisted language learning. However, existing techniques based on grammar rules or statistical machine translation are still not robust enough to tackle the common errors in sentences produced by second language learners. In this paper, a relative position language model and a parse template language model are proposed to complement traditional language modeling techniques in addressing this problem. A corpus of erroneous English-Chinese language transfer sentences along with their corrected counterparts is created and manually judged by human annotators. Experimental results show that compared to a state-of-the-art phrase-based statistical machine translation system, the error correction performance of the proposed approach achieves a significant improvement using human evaluation.
Sponsorship	IEEE Signal Processing Society
Starting Page	1170
Ending Page	1181
Page Count	12
File Size	675017
File Format	PDF
ISSN	15587916
Volume Number	18
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Natural languages Error correction Computer errors Humans Chaos Robustness System testing Councils Computer science Information management relative position Computer-Assisted Language Learning (CALL) error correction language transfer parse template
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering

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