Aspects of human speech understanding
Computer speech processing
Correction of phonographic errors in natural language interfaces
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Parsing Natural Language
Responding intelligently to unparsable inputs
Computational Linguistics
Computational Linguistics
EACL '87 Proceedings of the third conference on European chapter of the Association for Computational Linguistics
Recovery strategies for parsing extragrammatical language
Computational Linguistics - Special issue on ill-formed input
Parse fitting and prose fixing: getting a hold on ill-formedness
Computational Linguistics - Special issue on ill-formed input
Meta-rules as a basis for processing ill-formed input
Computational Linguistics - Special issue on ill-formed input
Preference semantics, ill-formedness, and metaphor
Computational Linguistics - Special issue on ill-formed input
Computational Linguistics - Special issue on ill-formed input
EACL '87 Proceedings of the third conference on European chapter of the Association for Computational Linguistics
Sentence-Level Evaluation Using Co-occurences of N-Grams
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Using mostly native data to correct errors in learners' writing: a meta-classifier approach
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Search right and thou shalt find...: using web queries for learner error detection
IUNLPBEA '10 Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Correcting different types of errors in texts
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
High-order sequence modeling for language learner error detection
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
A fast algorithm for words reordering based on language model
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Google books n-gram corpus used as a grammar checker
EACL 2012 Proceedings of the Second Workshop on Computational Linguistics and Writing (CLW 2012): Linguistic and Cognitive Aspects of Document Creation and Document Engineering
Towards advanced collocation error correction in Spanish learner corpora
Language Resources and Evaluation
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The Constituent Likelihood Automatic Word-tagging System (CLAWS) was originally designed for the low-level grammatical analysis of the million-word LOB Corpus of English text samples. CLAWS does not attempt a full parse, but uses a first-order Markov model of language to assign word-class labels to words. CLAWS can be modified to detect grammatical errors, essentially by flagging unlikely word-class transitions in the input text. This may seem to be an intuitively implausible and theoretically inadequate model of natural language syntax, but nevertheless it can successfully pinpoint most grammatical errors in a text. Several modifications to CLAWS have been explored. The resulting system cannot detect all errors in typed documents; but then neither do far more complex systems, which attempt a full parse, requiring much greater computation.