Applying a lightweight iterative merging chinese segmentation in web image annotation
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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With the rapid growth of the Internet, demand for retrieving similar strings to an input string has been increasing. Edit distance is helpful to retrieve necessary information from the large amount of data; edit distance is the similarity of two strings. Moreover, an input string must be compared with all strings in a dictionary in order to retrieve similar strings by computing edit distance, and this process is very time-consuming work. This paper proposes a structure of a dictionary for retrieving similar strings effectively and a method to output edit distance of strings in descending order at high speed. This method can retrieve all similar strings and the speed of this method is faster than that of a n-gram method.