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Eric Sven Ristad, Peter N. Yianilos, "Learning StringEdit Distance," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 5, pp. 522532, May, 1998.  
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@article{ 10.1109/34.682181, author = {Eric Sven Ristad and Peter N. Yianilos}, title = {Learning StringEdit Distance}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {20}, number = {5}, issn = {01628828}, year = {1998}, pages = {522532}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.682181}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Learning StringEdit Distance IS  5 SN  01628828 SP522 EP532 EPD  522532 A1  Eric Sven Ristad, A1  Peter N. Yianilos, PY  1998 KW  Stringedit distance KW  Levenshtein distance KW  stochastic transduction KW  syntactic pattern recognition KW  spelling correction KW  string correction KW  string similarity KW  string classification KW  pronunciation modeling KW  Switchboard corpus. VL  20 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Abstract—In many applications, it is necessary to determine the similarity of two strings. A widelyused notion of string similarity is the edit distance: The minimum number of insertions, deletions, and substitutions required to transform one string into the other. In this report, we provide a stochastic model for stringedit distance. Our stochastic model allows us to learn a stringedit distance function from a corpus of examples. We illustrate the utility of our approach by applying it to the difficult problem of learning the pronunciation of words in conversational speech. In this application, we learn a stringedit distance with nearly onefifth the error rate of the untrained Levenshtein distance. Our approach is applicable to any string classification problem that may be solved using a similarity function against a database of labeled prototypes.
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