DocumentCode :
3045253
Title :
Pairwise Learning to Rank for Search Query Correction
Author :
Novak, A. ; Sedivy, Jan
Author_Institution :
Dept. of Cybern., Czech Tech. Univ., Prague, Czech Republic
fYear :
2013
fDate :
13-16 Oct. 2013
Firstpage :
3054
Lastpage :
3059
Abstract :
This article introduces a new algorithm for a Search Query Spelling Correction System. It is based on learning to rank approach and allows to use large number of various signals leading to an improved accuracy. The performance will be tested against the conventional solution - the Noisy Channel Model. The new system was developed on a Czech Internet search query set, but the feature vector structure and the algorithm can be easily adapted for any other language when sufficient data is available. We will describe the algorithm details, the training and validation data sets. Further, we will discuss the selection and impact of the new feature vector signals.
Keywords :
Internet; learning (artificial intelligence); query processing; spelling aids; Czech Internet search query set; feature vector signals; feature vector structure; learning to rank model; noisy channel model; pairwise learning; search query correction rank; search query spelling correction system; Data models; Electronic publishing; Encyclopedias; Internet; Noise measurement; Vectors; feature vector; learning to rank; machine learning; noisy channel model; query correction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on
Conference_Location :
Manchester
Type :
conf
DOI :
10.1109/SMC.2013.521
Filename :
6722274
Link To Document :
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