AN EFFICIENT APPROACH TO QUERY REFORMULATION IN WEB SEARCH

Abstract

Wide range of problems regarding to natural language processing, mining of data, bioinformatics and information retrieval can be categorized as string transformation, the following task refers the same. If we give an input string, the system will generates the top k most equivalent output strings which are related to the same input string. In this paper we proposes a narrative and probabilistic method for the transformation of string, which is considered as accurate and also efficient. The approach uses a log linear model, along with the method used for training the model, and also an algorithm that generates the top k outcomes. Log linear method can be defined as restrictive possibility distribution of a result string and the set of rules for the alteration conditioned on key string. It is guaranteed that the resultant top k list will be generated using the algorithm for string generation which is based on pruning. The projected technique is applied to correct the spelling error in query as well as reformulation of queries in case of web based search. Spelling error correction, query reformulation for the related query is not considered in the previous work. Efficiency is not considered as an important issue taken into the consideration in earlier methods and was not focused on improvement of accuracy and efficiency in string transformation. The experimental outcomes on huge scale data show that the projected method is extremely accurate and also efficient.