Merging and Re-ranking Answers from Distributed Multiple Web Sources

Depending on questions, various answering methods and answer sources can be used. In this paper, we build a distributed QA system to handle different types of questions and web sources. When a user question is entered, the broker distributes the question over multiple sub-QAs according to question types. The selected sub-QAs find local optimal candidate answers, and then they are collected in to the answer manager. The merged candidates are re-ranked by adjusting confidence weights based on the question analysis result. The re-ranking algorithm aims to find global optimal answers. We borrow the concept from the margin and slack variables in SVM, and modify to project confidence weights into the same boundary by training. Several experimental results prove reliability of our proposed QA model.