Abstract: The present teaching relates to method, system, medium, and implementations for integrated targeting. An expert hierarchy is constructed with an initial expert layer with multiple initial experts and one or more augmented expert layers with each augmented expert therein augments, via machine learning, experts at any lower layer of the expert hierarchy. A nonlinear integration model is obtained, via machine learning, for combining expert predictions from experts in the expert hierarch based on an input to generate an integrated expert prediction in response to the input.

Abstract: The present teaching relates to method, system, medium, and implementations for predicting user segment. An expert hierarchy is created with an initial expert layer with multiple initial experts and at least one augmented expert layer. Each augmented expert layer has one or more augmented experts that are derived via machine training to augment at least the initial experts. When an input is received by the expert hierarchy, each of the experts, including initial and augmented, generates an expert prediction based on the input.

Abstract: The present teaching relates to method, system, medium, and implementations for integrating heterogeneous experts. A nonlinear integration model is characterized by a plurality of parameters and is configured for combining individual expert predictions to generate an integrated expert prediction Values of the plurality of parameters are learned based on training data as well as outputs from the respective plurality of experts generated based on the training data. When provided an input, individual expert predictions from different experts generated based on a given input are combined, using the nonlinear integration model, to derive an integrated expert prediction in response to the given input.

Abstract: The present teaching relates to method, system, medium, and implementations for text processing. Upon receiving input data including an original token and a ground truth token label for the original token, a manipulation is applied to the original token to generate a manipulated token based on which to generate manipulated input data. Training data is generated based on the manipulated input data, the ground truth token label, and a ground truth action that, when applied to the manipulated token, yields the original token with the ground truth token label. A text moderation model is trained based on the training data.

Abstract: The present teaching relates to method, system, medium, and implementations for text processing. Upon receiving input data including a plurality of text strings, a plurality of manipulated text strings are generated for each of the plurality of training text strings by first applying a manipulation to each of at least one original token in the text string to generate a manipulated token, where the original token has a ground truth token label and then determining, with respect to each manipulated token, a ground truth action which, when applied to the manipulated token, yields the original token with the ground truth token label.

Abstract: The present teaching relates to method, system, medium, and implementations for text processing. Upon receiving input data including a plurality of text strings, a plurality of manipulated text strings are generated for each of the plurality of training text strings by first applying a manipulation to each of at least one original token in the text string to generate a manipulated token, where the original token has a ground truth token label and then determining, with respect to each manipulated token, a ground truth action which, when applied to the manipulated token, yields the original token with the ground truth token label.

Abstract: A table corpus processing server identifies concepts within enterprise domain data. The table corpus processing server is configured to iteratively group values in a table corpus based on co-occurrence statistics to produce a candidate hierarchical tree. The candidate hierarchical tree is then summarized by selecting nodes that can best “describe” the original corpus, which leads to a small tree that often corresponds to desired concept hierarchies. The table corpus processing server employs a parallel dynamic programming approach that allows the disclosed embodiments to scale with amount of enterprise domain data being analyzed.