Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scoring concept terms using a deep network. One of the methods includes receiving an input comprising a plurality of features of a resource, wherein each feature is a value of a respective attribute of the resource; processing each of the features using a respective embedding function to generate one or more numeric values; processing the numeric values using one or more neural network layers to generate an alternative representation of the features, wherein processing the floating point values comprises applying one or more non-linear transformations to the floating point values; and processing the alternative representation of the input using a classifier to generate a respective category score for each category in a pre-determined set of categories, wherein each of the respective category scores measure a predicted likelihood that the resource belongs to the corresponding category.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: a deep network implemented in one or more computers that defines a plurality of layers of non-linear operations, wherein the deep network comprises: an embedding function layer configured to: receive an input comprising a plurality of features of a resource, wherein each feature is a value of a respective attribute of the resource, and process each of the features using a respective embedding function to generate one or more numeric values, and one or more neural network layers configured to: receive the numeric values, and process the numeric values to generate an alternative representation of the features of the resource, wherein processing the floating point values comprises applying one or more non-linear transformations to the floating point values; and a classifier configured to: process the alternative representation of the input to generate a respective category score for each category in a pre-determined set of categories, wherein each of the respective category scores measure a predicted likelihood that the resource belongs to the corresponding category.