According to an aspect, passage justification scoring is implemented by a processor executing computer readable instructions. The computer readable instructions includes creating a multi-layered neural network from domain knowledge and training the multi-layered neural network with labeled data and unlabeled data. The computer readable instructions further includes inputting at least one of an existing passage justification component and raw input data for a question and passage to the multi-layered neural network, extracting concepts determined to have passage justification with respect to a candidate answer contained in a respective passage, and creating a passage justification model from the extracted concepts and from passage justification ground truth.
Please help me write a proper abstract based on the patent claims. CLAIM: 1.-9. (canceled) 10. A system, comprising: a memory having computer readable computer instructions; and a processor for executing the computer readable instructions, the computer readable instructions including: 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. 11. The system of claim 10, wherein the existing passage justification components include at least one of: focus-answer type matching features; question-passage term matching features; question-passage parse matching features; question-passage dependency path matching features; question-passage relation matching features; and question-passage topic matching features. 12. The system of claim 10, wherein the features raw input data include at least one of: bag of words features for the question; bag of words features for the passage; typing features for the question; typing features for the passage; topic features for the question; topic features for the passage; Ngram features for the question; and Ngram features for the passage. 13. The system of claim 10, wherein the computer readable instructions further include generating the labeled data and the unlabeled data via: at least one of distant supervision using question-answer pairs and existing knowledge bases; and full supervision with manually annotated data. 14. The system of claim 10, wherein training the multi-layered neural network comprises using the labeled data with the multi-layered neural network to force the output of the multi-layered neural network to match corresponding labels of the labeled data. 15. The system of claim 10, wherein training the multi-layered neural network comprises using the unlabeled data with the multi-layered neural network to minimize data reconstruction errors. 16. The system of claim 10, wherein the extracting concepts determined to have passage justification includes using outputs from any selected one of the layers of the multi-layered neural network as concepts for input to a higher layer of the multi-layered neural network. 17. The system of claim 10, wherein the multi-layered neural network includes at least one of a convolutional neural network and a deep neural network. 18. The system of claim 10, wherein the multi-layered neural network includes a combination of stacked neural networks. 19. A computer program product comprising: a non-transitory storage medium readable by a processor and storing instructions for execution by the processor to perform 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. 20. The computer program product of claim 19, wherein the extracting concepts determined to have passage justification includes using outputs from any selected one of the layers of the multi-layered neural network as concepts for input to a higher layer of the multi-layered neural network.