Context based spelling correction
Information Processing and Management: an International Journal
Automatic spelling correction in scientific and scholarly text
Communications of the ACM
Computer programs for detecting and correcting spelling errors
Communications of the ACM
A technique for computer detection and correction of spelling errors
Communications of the ACM
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
The TREC-5 Confusion Track: Comparing Retrieval Methods for Scanned Text
Information Retrieval
Machine Learning
Automatic Rule Acquisition for Spelling Correction
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Named Entity recognition without gazetteers
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
ACL '94 Proceedings of the 32nd 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
Using contextual spelling correction to improve retrieval effectiveness in degraded text collections
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
An improved error model for noisy channel spelling correction
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Minimal commitment and full lexical disambiguation: balancing rules and hidden Markov Models
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Comparing corpora and lexical ambiguity
CompareCorpora '00 Proceedings of the Workshop on Comparing Corpora
Artificial Intelligence in Medicine
Towards Extraction of Conceptual Structures from Electronic Health Records
ICCS '09 Proceedings of the 17th International Conference on Conceptual Structures: Conceptual Structures: Leveraging Semantic Technologies
Methodological Review: What can natural language processing do for clinical decision support?
Journal of Biomedical Informatics
Artificial Intelligence in Medicine
Medical (visual) information retrieval
PROMISE'12 Proceedings of the 2012 international conference on Information Retrieval Meets Information Visualization
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In this article, we show how a set of natural language processing (NLP) tools can be combined to improve the processing of clinical records. The study concentrates on improving spelling correction, which is of major importance for quality control in the electronic patient record (EPR). As first task, we report on the design of an improved interactive tool for correcting spelling errors. Unlike traditional systems, the linguistic context (both semantic and syntactic) is used to improve the correction strategy. The system is organized along three modules. Module 1 is based on a classical spelling checker, it means that it is context-independent and simply measures a string-edit-distance between a misspelled word and a list of well-formed words. Module 2 attempts to rank more relevantly the set of candidates provided by the first module using morpho-syntactic disambiguation tools. Module 3 processes words with the same part-of-speech (POS) and apply word-sense (WS) disambiguation in order to rerank the set of candidates. As second task, we show how this improved interactive spell checker can be cast as a fully automatic system by adjunction of another NLP module: a named-entity (NE) extractor, i.e. a tool able to identify words as such patient and physician names. This module is used to avoid replacement of named-entities when the system is not used in an interactive mode. Results confirm that using the linguistic context can improve interactive spelling correction, and justify the use of named-entity recognizer to conduct fully automatic spelling correction. It is concluded that NLP is mature enough to help information processing in EPR.