According to an aspect, a term saliency model is trained to identify salient terms that provide supporting evidence of a candidate answer in a question answering computer system based on a training dataset. The question answering computer system can perform term saliency weighting of a candidate passage to identify one or more salient terms and term weights in the candidate passage based on the term saliency model. The one or more salient terms and term weights can be provided to at least one passage scorer of the question answering computer system to determine whether the candidate passage is justified as providing supporting evidence of the candidate answer.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: training a term saliency model to identify salient terms that provide supporting evidence of a candidate answer in a question answering computer system based on a training dataset; performing term saliency weighting, by the question answering computer system, of a candidate passage to identify one or more salient terms and term weights in the candidate passage based on the term saliency model; and providing the one or more salient terms and term weights to at least one passage scorer of the question answering computer system to determine whether the candidate passage is justified as providing supporting evidence of the candidate answer. 2. The method of claim 1, wherein the training dataset comprises a plurality of annotated candidate passage and question pairs. 3. The method of claim 2, wherein the training further comprises: performing syntactic and semantic annotation on at least a subset of the annotated candidate passages to produce an annotated set of terms; performing feature extraction on the annotated set of terms based on a plurality of features and a corresponding question from the annotated candidate passage and question pairs to determine a plurality of feature scores; and applying a machine learning algorithm to train the term saliency model based on the extracted features and identified salient terms from the annotated candidate passages. 4. The method of claim 3, wherein the features comprise a combination of syntactic, contextual, and semantic features. 5. The method of claim 3, wherein the training further comprises: determining the feature scores for a first set of the features from the annotated set of terms in a first training stage; identifying potentially salient terms based on the feature scores from the first training stage; and determining the feature scores for a second set of the features of neighboring terms of the potentially salient terms, wherein the first set of the features partially overlaps the second set of the features. 6. The method of claim 1, wherein the candidate passage is accessed from an application dataset comprising a plurality of candidate passage and question pairs. 7. The method of claim 6, wherein performing term saliency weighting further comprises: performing syntactic and semantic annotation on the candidate passage to produce an annotated set of terms; performing feature extraction on the annotated set of terms based on a plurality of features and a corresponding question from the candidate passage and question pairs to determine a plurality of feature scores; and applying the term saliency model to the extracted features to identify one or more salient terms and term weights from the candidate passage. 8. The method of claim 7, further comprising: aggregating scores from the at least one passage scorer to determine whether the candidate passage is justified as providing supporting evidence of the candidate answer. 9. A computer program product comprising: a tangible storage medium readable by a processing circuit of a question answering computer system and storing instructions for execution by the processing circuit to perform a method comprising: training a term saliency model to identify salient terms that provide supporting evidence of a candidate answer based on a training dataset; performing term saliency weighting of a candidate passage to identify one or more salient terms and term weights in the candidate passage based on the term saliency model; and providing the one or more salient terms and term weights to at least one passage scorer of the question answering computer system to determine whether the candidate passage is justified as providing supporting evidence of the candidate answer. 10. The computer program product of claim 9, wherein the training dataset comprises a plurality of annotated candidate passage and question pairs, and the training further comprises: performing syntactic and semantic annotation on at least a subset of the annotated candidate passages to produce an annotated set of terms; performing feature extraction on the annotated set of terms based on a plurality of features and a corresponding question from the annotated candidate passage and question pairs to determine a plurality of feature scores; and applying a machine learning algorithm to train the term saliency model based on the extracted features and identified salient terms from the annotated candidate passages. 11. The computer program product of claim 10, wherein the features comprise a combination of syntactic, contextual, and semantic features. 12. The computer program product of claim 10, wherein the training further comprises: determining the feature scores for a first set of the features from the annotated set of terms in a first training stage; identifying potentially salient terms based on the feature scores from the first training stage; and determining the feature scores for a second set of the features of neighboring terms of the potentially salient terms, wherein the first set of the features partially overlaps the second set of the features. 13. The computer program product of claim 9, wherein the candidate passage is accessed from an application dataset comprising a plurality of candidate passage and question pairs, and performing term saliency weighting further comprises: performing syntactic and semantic annotation on the candidate passage to produce an annotated set of terms; performing feature extraction on the annotated set of terms based on a plurality of features and a corresponding question from the candidate passage and question pairs to determine a plurality of feature scores; and applying the term saliency model to the feature scores to identify one or more salient terms and term weights from the candidate passage. 14. The computer program product of claim 9, wherein the method further comprises: aggregating scores from the at least one passage scorer to determine whether the candidate passage is justified as providing supporting evidence of the candidate answer. 15. A system comprising: a memory having computer readable instructions; and a processor for executing the computer readable instructions, the computer readable instructions including: training a term saliency model to identify salient terms that provide supporting evidence of a candidate answer in a question answering computer system based on a training dataset; performing term saliency weighting, by the question answering computer system, of a candidate passage to identify one or more salient terms and term weights in the candidate passage based on the term saliency model; and providing the one or more salient terms and term weights to at least one passage scorer of the question answering computer system to determine whether the candidate passage is justified as providing supporting evidence of the candidate answer. 16. The system of claim 15, wherein the training dataset comprises a plurality of annotated candidate passage and question pairs, and the training further comprises: performing syntactic and semantic annotation on at least a subset of the annotated candidate passages to produce an annotated set of terms; performing feature extraction on the annotated set of terms based on a plurality of features and a corresponding question from the annotated candidate passage and question pairs to determine a plurality of feature scores; and applying a machine learning algorithm to train the term saliency model based on the extracted features and identified salient terms from the annotated candidate passages. 17. The system of claim 16, wherein the features comprise a combination of syntactic, contextual, and semantic features. 18. The system of claim 16, wherein the training further comprises: determining the feature scores for a first set of the features from the annotated set of terms in a first training stage; identifying potentially salient terms based on the feature scores from the first training stage; and determining the feature scores for a second set of the features of neighboring terms of the potentially salient terms, wherein the first set of the features partially overlaps the second set of the features. 19. The system of claim 18, wherein the candidate passage is accessed from an application dataset comprising a plurality of candidate passage and question pairs, and performing term saliency weighting further comprises: performing syntactic and semantic annotation on the candidate passage to produce an annotated set of terms; performing feature extraction on the annotated set of terms based on a plurality of features and a corresponding question from the candidate passage and question pairs to determine a plurality of feature scores; and applying the term saliency model to the extracted features to identify one or more salient terms and term weights from the candidate passage. 20. The system of claim 15, wherein the computer readable instructions further comprise: aggregating scores from the at least one passage scorer to determine whether the candidate passage is justified as providing supporting evidence of the candidate answer.