TY - GEN SP - 74 T3 - Lecture Notes in Computer Science SN - 1611-3349 N2 - Analysis of unstructured data may be inefficient in the presence of spelling errors. Existing approaches use string similarity methods to search for valid words within a text, with a supporting dictionary. However, they are not rich enough to encode phonetic information to assist the search. In this paper, we present a novel approach for efficiently perform phonetic similarity search over large data sources, that uses a data structure called PhoneticMap to encode language-specific phonetic information. We validate our approach through an experiment over a data set using a Portuguese variant of a well-known repository, to automatically correct words with spelling errors. AV - public N1 - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions. ID - discovery10065716 UR - https://doi.org/10.1007/978-3-319-10085-2_6 PB - Springer EP - 81 A1 - Tissot, H A1 - Peschl, G A1 - Del Fabro, MD KW - Phonetic Similarity KW - String Similarity KW - Fast Search TI - Fast phonetic similarity search over large repositories CY - Cham, Switzerland Y1 - 2014/01/01/ ER -