1. A computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement a mixed-language question answering supplement system, the method comprising: receiving a question in a target language; applying natural language processing to parse the question into at least one focus; for each focus, determining if one or more target language verbs share direct syntactic dependency with the focus; for each of the one or more verbs sharing direct syntactic dependency, determining if one or more target language entities share direct syntactic dependency with the verb; determining one or more Abstract Universal Verbal Types associated with each verb; for each of the one or more Abstract Universal Verbal Types, determining whether a dependency between a source language entity and a source language verb is of the same type as the dependency between the target language verb and the target language entity comprising: using a cognitive system to generate a plurality of reasoning algorithms to analyze the source language and the target language, wherein each reasoning algorithm generates a dependency score; training a statistical model employed by a question answer pipeline; applying the trained statistical model to determine a weight for each dependency score; applying the weight to each dependency score to generate weighted dependency scores; and processing the weighted dependency scores with the statistical model to generate one or more confidence scores measuring dependency; if the dependency is similar, returning the source language entity as a member of a set; and if the set is full, returning an answer in the target language to the question in the target language.
1. A computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement a mixed-language question answering supplement system, the method comprising: receiving a question in a target language; applying natural language processing to parse the question into at least one focus; for each focus, determining if one or more target language verbs share direct syntactic dependency with the focus; for each of the one or more verbs sharing direct syntactic dependency, determining if one or more target language entities share direct syntactic dependency with the verb; determining one or more Abstract Universal Verbal Types associated with each verb; for each of the one or more Abstract Universal Verbal Types, determining whether a dependency between a source language entity and a source language verb is of the same type as the dependency between the target language verb and the target language entity comprising: using a cognitive system to generate a plurality of reasoning algorithms to analyze the source language and the target language, wherein each reasoning algorithm generates a dependency score; training a statistical model employed by a question answer pipeline; applying the trained statistical model to determine a weight for each dependency score; applying the weight to each dependency score to generate weighted dependency scores; and processing the weighted dependency scores with the statistical model to generate one or more confidence scores measuring dependency; if the dependency is similar, returning the source language entity as a member of a set; and if the set is full, returning an answer in the target language to the question in the target language. 8. The method as recited in claim 1 , further comprising: assigning a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the target language.