Determining a relevance function based on a query error derived using a structured output learning technique
First Claim
1. A method of determining a relevance function, comprising:
- determining one or more features to be used as predictor variables in the construction of a relevance function;
parameterizing the relevance function by one or more coefficients at least partially using hardware;
defining a query error that is a continuous upper bound on an ideal query error, the ideal query error being a difference between a relevance measure on a ranking generated by the relevance function and a ranking based on a training set, said defining including deriving the query error using a structured output learning technique, wherein the query error is defined as a maximum over a set of permutations; and
determining values for the coefficients of the relevance function by gradient descent to substantially minimize an objective function that depends on the defined query error.
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Abstract
Methods, systems, and apparatuses for generating relevance functions for ranking documents obtained in searches are provided. One or more features to be used as predictor variables in the construction of a relevance function are determined. The relevance function is parameterized by one or more coefficients. An ideal query error is defined that measures, for a given query, a difference between a ranking generated by the relevance function and a ranking based on a training set. According to a structured output learning framework, values for the coefficients of the relevance function are determined to substantially minimize an objective function that depends on a continuous upper bound of the defined ideal query error. The query error is determined using a structured output learning technique. The query error is defined as a maximum over a set of permutations.
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Citations
16 Claims
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1. A method of determining a relevance function, comprising:
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determining one or more features to be used as predictor variables in the construction of a relevance function; parameterizing the relevance function by one or more coefficients at least partially using hardware; defining a query error that is a continuous upper bound on an ideal query error, the ideal query error being a difference between a relevance measure on a ranking generated by the relevance function and a ranking based on a training set, said defining including deriving the query error using a structured output learning technique, wherein the query error is defined as a maximum over a set of permutations; and determining values for the coefficients of the relevance function by gradient descent to substantially minimize an objective function that depends on the defined query error. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system for determining a relevance function, comprising:
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at least one computer that includes hardware; a relevance function constructor implemented at least partially using the hardware that is configured to construct a relevance function based on one or more features used as predictor variables and one or more coefficients; and a relevance function tuner configured to determine values for the one or more coefficients of the relevance function by gradient descent to substantially minimize an objective function that depends on a query error, wherein the query error is a continuous upper bound on an ideal query error, the ideal query error being a difference between a relevance measure on a ranking generated by the relevance function and a ranking based on a training set, wherein the query error is determined by a structured output learning technique, wherein the query error is defined as a maximum over a set of permutations. - View Dependent Claims (10, 11, 12, 13, 14, 15)
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16. A computer program product comprising a computer usable medium having computer readable program code means embodied in said medium for determining a relevance function, comprising:
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a first computer readable program code means for enabling a processor to determine values for one or more coefficients of a relevance function that is based on one or more features used as predictor variables and the one or more coefficients, wherein the first computer readable program code means is configured to enable the processor to determine values for the one or more coefficients by gradient descent to substantially minimize an objective function that depends on a query error, wherein the query error is a continuous upper bound on an ideal query error, the ideal query error being a difference between a relevance measure on a ranking generated by the relevance function and a ranking based on a training set; wherein the first computer readable program code means comprises; a second computer readable program code means for enabling a processor to determine the query error using a structured output learning technique, wherein the query error is defined as a maximum over a set of permutations.
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Specification