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• W2027271985 startingPage "1063" @default.
• W2027271985 abstract "Many problems in natural language processing, data mining, information retrieval, and bioinformatics can be formalized as string transformation, which is a task as follows. Given an input string, the system generates the k most likely output strings corresponding to the input string. This paper proposes a novel and probabilistic approach to string transformation, which is both accurate and efficient. The approach includes the use of a log linear model, a method for training the model, and an algorithm for generating the top k candidates, whether there is or is not a predefined dictionary. The log linear model is defined as a conditional probability distribution of an output string and a rule set for the transformation conditioned on an input string. The learning method employs maximum likelihood estimation for parameter estimation. The string generation algorithm based on pruning is guaranteed to generate the optimal top k candidates. The proposed method is applied to correction of spelling errors in queries as well as reformulation of queries in web search. Experimental results on large scale data show that the proposed approach is very accurate and efficient improving upon existing methods in terms of accuracy and efficiency in different settings." @default.
• W2027271985 created "2016-06-24" @default.
• W2027271985 creator A5060955767 @default.
• W2027271985 creator A5074586582 @default.
• W2027271985 creator A5080733133 @default.
• W2027271985 creator A5086275760 @default.
• W2027271985 date "2014-05-01" @default.
• W2027271985 modified "2023-10-16" @default.
• W2027271985 title "A Probabilistic Approach to String Transformation" @default.
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• W2027271985 doi "https://doi.org/10.1109/tkde.2013.11" @default.
• W2027271985 hasPublicationYear "2014" @default.
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