Turning down the noise in the blogosphere
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Design challenges and misconceptions in named entity recognition
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Understanding the value of features for coreference resolution
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning and inference with constraints
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Training paradigms for correcting errors in grammar and usage
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Object search: supporting structured queries in web search engines
SS '10 Proceedings of the NAACL HLT 2010 Workshop on Semantic Search
Generating confusion sets for context-sensitive error correction
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Algorithm selection and model adaptation for ESL correction tasks
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
They can help: using crowdsourcing to improve the evaluation of grammatical error detection systems
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
University of Illinois system in HOO text correction shared task
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
Informing determiner and preposition error correction with word clusters
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Bucking the trend: improved evaluation and annotation practices for ESL error detection systems
Language Resources and Evaluation
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Many recent advances in complex domains such as Natural Language Processing (NLP) have taken a discriminative approach in conjunction with the global application of structural and domain specific constraints. We introduce LBJ, a new modeling language for specifying exact inference systems of this type, combining ideas from machine learning, optimization, First Order Logic (FOL), and Object Oriented Programming (OOP). Expressive constraints are specified declaratively as arbitrary FOL formulas over functions and objects. The language's run-time library translates them to a mathematical programming representation from which an exact solution is computed. In addition, the compiler leverages an existing OOP language: objects and functions are grounded as the OOP objects and methods that encapsulate the user's data.