| 摘要 |
In one implementation, a method is provided for increasing relevance of database search results. The method includes receiving a subject query string and determining a trained edit distance between the subject query string and a candidate string using trained cost factors derived from a training set of labeled query transformations. A trained cost factor includes a conditional probability for mutations in labeled non-relevant query transformations and a conditional probability for mutations in labeled relevant query transformations. The candidate string is evaluated the for selection based on the trained edit distance. In some implementations, the cost factors may take into account the context of a mutation. As such, in some implementations multi-dimensional matrices are utilized which include the trained cost factors.
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