Techniques for automatically correcting words in text
ACM Computing Surveys (CSUR)
Finite-State Language Processing
Finite-State Language Processing
Automatic Rule Acquisition for Spelling Correction
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Regular expressions for language engineering
Natural Language Engineering
Meta-rules as a basis for processing ill-formed input
Computational Linguistics - Special issue on ill-formed input
Integrated control of chart items for error repair
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Towards a single proposal in spelling correction
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Combining stochastic and rule-based methods for disambiguation in agglutinative languages
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Some chart-based techniques for parsing ill-formed input
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Combining Trigram-based and feature-based methods for context-sensitive spelling correction
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Parsing Ill-Formed Text Using an Error Grammar
Artificial Intelligence Review
Syntactic error detection and correction in date expressions using finite-state transducers
Natural Language Engineering
Design and development of a system for the detection of agreement errors in basque
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Hi-index | 0.00 |
This paper presents a parsing system for the detection of syntactic errors. It combines a robust partial parser which obtains the main sentence components and a finite-state parser used for the description of syntactic error patterns. The system has been tested on a corpus of real texts, containing both correct and incorrect sentences, with promising results.