Abstract: Described is a technology for measuring the similarity between two objects (e.g., documents), via a framework that learns the term-weighting function from training data, e.g., labeled pairs of objects, to develop a learned model. A learning procedure tunes the model parameters by minimizing a defined loss function of the similarity score. Also described is using the learning procedure and learned model to detect near duplicate documents.

Abstract: A method and system for assignee identification of speech includes defining several time intervals and utilizing one or more function evaluations to classify each of the several participants as addressing speech to an automated character or not addressing speech to the automated character during each of the several time intervals. A first function evaluation includes computing values for a predetermined set of features for each of the participants during a particular time interval and assigning a first addressing status to each of the several participants in the particular time interval, based on the values of each of the predetermined sets of features determined during the particular time interval. A second function evaluation may assign a second addressing status to each of the several participants in the particular time interval utilizing results of the first function evaluation for the particular time interval and for one or more additional contiguous time intervals.

Abstract: Described is a technology for measuring the similarity between two objects (e.g., documents), via a framework that learns the term-weighting function from training data, e.g., labeled pairs of objects, to develop a learned model. A learning procedure tunes the model parameters by minimizing a defined loss function of the similarity score. Also described is using the learning procedure and learned model to detect near duplicate documents.