Query Reformulation Using String Transformation

International Journal of Computer Science and Engineering

© 2014 by SSRG - IJCSE Journal

Volume 1 Issue 9

Year of Publication : 2014

Authors : Sheetal More

10.14445/23488387/IJCSE-V1I9P107

How to Cite?

Sheetal More, "Query Reformulation Using String Transformation," SSRG International Journal of Computer Science and Engineering , vol. 1,  no. 9, pp. 1-5, 2014. Crossref, https://doi.org/10.14445/23488387/IJCSE-V1I9P107

Abstract:

Generally there is a need to change the input string in data mining, natural language processing, information retrieval and bioinformatics. Many times the user is not from the technical background so he may enter the incorrect input string. Depending on the input string the system will generate some topmost k output strings. Most of the time for better results of the search, there is need to transform the input string. Sometimes the user also enters the shortforms that is abbreviations for the search in that case there is need of transforming these abbreviations into their original forms or meanings. Many times while entering the input string user does the spelling mistakes depending upon the pronunciations. So in this case there is need to correct these spelling mistakes and then search for the results. Thus there is need of converting abbreviations into their original form, correction of spelling errors and also replacing the word with its synonym if needed for better search results. Thus these conversions of strings can be stated as string transformation. String transformation can be conducted in two different ways depending upon the use of dictionary that is whether the dictionary is used or not. In this paper log linear model is expressed in terms of an input and output strings. The paper shows an approach for string transformation which is both accurate and efficient. The proposed method is applicable for string transformation and reformation of query.

Keywords:

 Log Linear Model, Spelling Error Correction, String Transformation, Query Reformulation.

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