1. A method, comprising: creating a multi-layered neural network from domain knowledge; training the multi-layered neural network with labeled data and unlabeled data; inputting at least one of an existing passage justification component and raw input data for a question and passage to the trained multi-layered neural network; extracting, via the trained multi-layered neural network and from results of inputting the at least one existing passage justification component and raw input data for a question and passage, concepts determined to have passage justification; creating, via machine learning, a passage justification model from the extracted concepts and from passage justification; and scoring, via the passage justification model, the extracted concepts.
1. A method, comprising: creating a multi-layered neural network from domain knowledge; training the multi-layered neural network with labeled data and unlabeled data; inputting at least one of an existing passage justification component and raw input data for a question and passage to the trained multi-layered neural network; extracting, via the trained multi-layered neural network and from results of inputting the at least one existing passage justification component and raw input data for a question and passage, concepts determined to have passage justification; creating, via machine learning, a passage justification model from the extracted concepts and from passage justification; and scoring, via the passage justification model, the extracted concepts. 8. The method of claim 1 , wherein the multi-layered neural network includes at least one of a convolutional neural network and a deep neural network.