NDLI: A Probabilistic Framework for Chinese Spelling Check

Please wait, while we are loading the content...

A Probabilistic Framework for Chinese Spelling Check

Content Provider	ACM Digital Library
Author	Wang, Hsin-Min Chen, Hsin-Hsi Chen, Kuan-Yu
Copyright Year	2015
Abstract	Chinese spelling check (CSC) is still an unsolved problem today since there are many homonymous or homomorphous characters. Recently, more and more CSC systems have been proposed. To the best of our knowledge, language modeling is one of the major components among these systems because of its simplicity and moderately good predictive power. After deeply analyzing the school of research, we are aware that most of the systems only employ the conventional $\textit{n}-gram$ language models. The contributions of this article are threefold. First, we propose a novel probabilistic framework for CSC, which naturally combines several important components, such as the substitution model and the language model, to inherit their individual merits as well as to overcome their limitations. Second, we incorporate the topic language models into the CSC system in an unsupervised fashion. The topic language models can capture the long-span semantic information from a word (character) string while the conventional $\textit{n}-gram$ language models can only preserve the local regularity information. Third, we further integrate Web resources with the proposed framework to enhance the overall performance. Our rigorously empirical experiments demonstrate the consistent and utility performance of the proposed framework in the CSC task.
Starting Page	1
Ending Page	17
Page Count	17
File Format	PDF
ISSN	23754699
e-ISSN	23754702
DOI	10.1145/2826234
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 Language model Probabilistic Spelling check Topic modeling
Content Type	Text
Resource Type	Article
Subject	Computer Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Hybrid Ranking Approach to Chinese Spelling Check

RSpell: Retrieval-augmented Framework for Domain Adaptive Chinese Spelling Check

A Chinese Spelling Check Framework Based on Reverse Contrastive Learning

A hybrid language model based on a recurrent neural network and probabilistic topic modeling

Correcting Chinese Spelling Errors with Word Lattice Decoding

A study of language modeling for chinese spelling check.

Chinese Spelling Checker Based on an Inverted Index List with a Rescoring Mechanism

A Comprehensive Evaluation and Analysis Study for Chinese Spelling Check

A chinese ocr spelling check approach based on statistical language models ∗.

A Probabilistic Framework for Chinese Spelling Check

Similar Documents

A Hybrid Ranking Approach to Chinese Spelling Check

RSpell: Retrieval-augmented Framework for Domain Adaptive Chinese Spelling Check

A Chinese Spelling Check Framework Based on Reverse Contrastive Learning

A hybrid language model based on a recurrent neural network and probabilistic topic modeling

Correcting Chinese Spelling Errors with Word Lattice Decoding

A study of language modeling for chinese spelling check.

Chinese Spelling Checker Based on an Inverted Index List with a Rescoring Mechanism

A Comprehensive Evaluation and Analysis Study for Chinese Spelling Check

A chinese ocr spelling check approach based on statistical language models ∗.

A Probabilistic Framework for Chinese Spelling Check