With the emergence of community-based question answering (cQA) services, question retrieval has become an integral part of information and knowledge acquisition. Though existing information retrieval (IR) technologies have been found to be successful for document retrieval, they are less effective for question retrieval due to the inherent characteristics of questions, which have shorter texts. One of the major common drawbacks for the term weighting-based question retrieval models is that they overlook the relations between term pairs when computing their weights. To tackle this problem, we propose a novel term weighting scheme by incorporating the dependency relation cues between term pairs. Given a question, we first construct a dependency graph and compute the relation strength between each term pairs. Next, based on the dependency relation scores, we refine the initial term weights estimated by conventional term weighting approaches. We demonstrate that the proposed term weighting scheme can be seamlessly integrated with popular question retrieval models. Comprehensive experiments well validate our proposed scheme and show that it achieves promising performance as compared to the state-of-the-art methods.
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