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  1. Transactions on Asian and Low-Resource Language Information Processing (TALLIP)
  2. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) : Volume 14
  3. Issue 4(Special Issue on Chinese Spell Checking), October 2015
  4. Correcting Chinese Spelling Errors with Word Lattice Decoding
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ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) : Volume 16
ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) : Volume 15
ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) : Volume 14
Issue 4(Special Issue on Chinese Spell Checking), October 2015
TALLIP Perspectives: Editorial Commentary: The State of the Journal
Introduction to the Special Issue on Chinese Spell Checking
A Probabilistic Framework for Chinese Spelling Check
A Hybrid Ranking Approach to Chinese Spelling Check
Chinese Spelling Checker Based on an Inverted Index List with a Rescoring Mechanism
Correcting Chinese Spelling Errors with Word Lattice Decoding
Issue 3, June 2015
Issue 2, March 2015
Issue 1, January 2015

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Correcting Chinese Spelling Errors with Word Lattice Decoding

Content Provider ACM Digital Library
Author Bai, Ming-Hong Chen, Keh-Jiann Hsieh, Yu-Ming Huang, Shu-Ling
Copyright Year 2015
Abstract Chinese spell checkers are more difficult to develop because of two language features: 1) there are no word boundaries, and a character may function as a word or a word morpheme; and 2) the Chinese character set contains more than ten thousand characters. The former makes it difficult for a spell checker to detect spelling errors, and the latter makes it difficult for a spell checker to construct error models. We develop a word lattice decoding model for a Chinese spell checker that addresses these difficulties. The model performs word segmentation and error correction simultaneously, thereby solving the word boundary problem. The model corrects nonword errors as well as real-word errors. In order to better estimate the error distribution of large character sets for error models, we also propose a methodology to extract spelling error samples automatically from the Google web 1T corpus. Due to the large quantity of data in the Google web 1T corpus, many spelling error samples can be extracted, better reflecting spelling error distributions in the real world. Finally, in order to improve the spell checker for real applications, we produce n-best suggestions for spelling error corrections. We test our proposed approach with the Bakeoff 2013 CSC Datasets; the results show that the proposed methods with the error model significantly outperform the performance of Chinese spell checkers that do not use error models.
Starting Page 1
Ending Page 23
Page Count 23
File Format PDF
ISSN 23754699
e-ISSN 23754702
DOI 10.1145/2791389
Volume Number 14
Issue Number 4
Journal ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-11-11
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Chinese spelling error checking Computer-assisted language learning Noisy channel model Unknown word detection Word lattice Word segmentation
Content Type Text
Resource Type Article
Subject Computer Science
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