Abstract: A method and a system for response evaluation of users from electronic documents are disclosed. In an embodiment, one or more questions and a first response pertaining to each of the one or more questions are extracted from one or more first electronic documents. Further, a second response pertaining to each of the one or more extracted questions and metadata are extracted from one or more second electronic documents. For the second response pertaining to each of the one or more extracted questions, a score is determined based on one or more similarity measures that correspond to a category of each of the one or more extracted questions. Thereafter, the response evaluation is rendered on a user interface displayed on a display screen. The response evaluation comprises at least the determined score for the second response pertaining to each of the one or more extracted questions.

Abstract: A method and a system for response evaluation of users from electronic documents are disclosed. In an embodiment, one or more questions and a first response pertaining to each of the one or more questions are extracted from one or more first electronic documents. Further, a second response pertaining to each of the one or more extracted questions and metadata are extracted from one or more second electronic documents. For the second response pertaining to each of the one or more extracted questions, a score is determined based on one or more similarity measures that correspond to a category of each of the one or more extracted questions. Thereafter, the response evaluation is rendered on a user interface displayed on a display screen. The response evaluation comprises at least the determined score for the second response pertaining to each of the one or more extracted questions.

Abstract: An adaptation method includes using a first classifier trained on projected representations of labeled objects from a first domain to predict pseudo-labels for unlabeled objects in a second domain, based on their projected representations. A classifier ensemble is iteratively learned. The ensemble includes a weighted combination of the first classifier and a second classifier. This includes training the second classifier on the original representations of the unlabeled objects for which a confidence for respective pseudo-labels exceeds a threshold. A classifier ensemble is constructed as a weighted combination of the first classifier and the second classifier. Pseudo-labels are predicted for the remaining original representations of the unlabeled objects with the classifier ensemble and weights of the first and second classifiers in the classifier ensemble are adjusted. As the iterations proceed, the unlabeled objects progressively receive pseudo-labels which can be used for retraining the second classifier.