Program: Automated Library and Information Systems
Techniques for automatically correcting words in text
ACM Computing Surveys (CSUR)
Finding approximate matches in large lexicons
Software—Practice & Experience
String alignment with substitution, insertion, deletion, squashing, and expansion operations
Information Sciences—Informatics and Computer Science: An International Journal
An algorithm to align words for historical comparison
Computational Linguistics
The String-to-String Correction Problem
Journal of the ACM (JACM)
Very fast and simple approximate string matching
Information Processing Letters
ACM Computing Surveys (CSUR)
Topic segmentation: algorithms and applications
Topic segmentation: algorithms and applications
Bitext maps and alignment via pattern recognition
Computational Linguistics
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
Identifying cognates by phonetic and semantic similarity
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
n-Gram Statistics for Natural Language Understanding and Text Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Objective: Many hundreds of drugs have names that either look or sound so much alike that doctors, nurses and pharmacists can get them confused, dispensing the wrong one in errors that can injure or even kill patients. Methods and material: We propose to address the problem through the application of two new methods-one based on orthographic similarity (''look-alike''), and the other based on phonetic similarity (''sound-alike''). In order to compare the effectiveness of the new methods for identifying confusable drug names with other known similarity measures, we developed a novel evaluation methodology. Results: We show that the new orthographic measure (BI-SIM) outperforms other commonly used measures of similarity on a set containing both look-alike and sound-alike pairs, and that a new feature-based phonetic approach (ALINE) outperforms orthographic approaches on a test set containing solely sound-alike pairs. However, an approach that combines several different measures achieves the best results on two test sets. Conclusion: Our system is currently used as the basis of a system developed for the U.S. Food and Drug Administration for detection of confusable drug names.