Approximate String Matching
Graham A Stephen
- 01 Oct 1994
- pp 111-189
113
About: The article was published on 01 Oct 1994. and is currently open access. The article focuses on the topics: Commentz-Walter algorithm & Boyer–Moore string search algorithm.
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Citations
A Comparison of Personal Name Matching: Techniques and Practical Issues
Peter Christen
- 18 Dec 2006
TL;DR: The characteristics of personal names are discussed and potential sources of variations and errors are presented and a comprehensive number of commonly used, as well as some recently developed name matching techniques are overview.
Computation of normalized edit distance and applications
Andrés Marzal,Enrique Vidal +1 more
TL;DR: Experiments in hand-written digit recognition are presented, revealing that the normalized edit distance consistently provides better results than both unnormalized or post-normalized classical edit distances.
Phonetic string matching: lessons from information retrieval
Justin Zobel,Philip W. Dart +1 more
- 18 Aug 1996
TL;DR: The parallels between information retrieval and phonetic matching are explained, and the new phonetics matching techniques described are compared with existing techniques to demonstrate that the new techniques are superior.
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Melodic matching techniques for large music databases
Alexandra L. Uitdenbogerd,Justin Zobel +1 more
- 30 Oct 1999
TL;DR: A three-stage framework for matching pieces of music is proposed and it is shown that, by instantiating the framework with appropriate music manipulation and comparison techniques, Pieces of music that match a query can be identified in a large collection.
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A model for compound type changes encountered in schema evolution
TL;DR: Tess (Type Evolution Software System) is built that uses a model of type changes involving multiple types to recognize type changes by comparing schemas and then produces a transformer that can update data in a database to correspond to a newer version of the schema.
References
Retrieval effectiveness of proper name search methods
TL;DR: These measures deal with phonetic similarity, typing errors and plain string similarity and it is shown experimentally that all three approaches lead to significantly higher retrieval quality than plain identity.
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