The Method is applied to correction of spelling errors in queries Reformulation

This paper addresses string transformation, which is an essential problem, in many applications. In natural language processing, pronunciation generation, spelling error correction, word transliteration, and word stemming can all be formalized as string transformation. String transformation can also be used in query reformulation and query suggestion in search. In data mining, string transformation can be employed in the mining of synonyms and database record matching. As many of the above are online applications, the transformation must be conducted not only accurately but also efficiently. String transformation can be defined in the following way. Given an input string and a set of operators, we are able to transform the input string to the k most likely output strings by applying a number of operators. Here the strings can be strings of words, characters, or any type of tokens. Each operator is a transformation rule that defines the replacement of a substring with another substring. The likelihood of transformation can represent similarity, relevance, and association between two strings in a specific application. Although certain progress has been made, further investigation of the task is still necessary, particularly from the viewpoint of enhancing both accuracy and efficiency, which is precisely the goal of this work. Spelling error correction normally consists of candidate generation and candidate selection. The former task is an example of string transformation. Candidate generation is usually only concerned with a single word. For single-word candidate generation, a rule-based approach is commonly used. The use of edit distance is a typical approach, which exploits operations of character deletion, insertion and substitution. Some methods generate candidates within a fixed range of edit distance or different ranges for strings with different lengths . Other methods learn weighted edit distance to enhance the representation power