Abstract
Recommending unanswered questions to answer experts is an important mechanism in User-Interactive Question Answering (UIQA) services and is helpful to reduce asker's waiting time and obtain high-quality answers. In this paper, we address the task of identifying answer experts in UIQA services with semantic information extracted from user interaction behaviors. We first construct the user question-answer interaction graph through direct semantic links and latent links extracted from the records of question sessions and user profiles. After that, two expert-finding approaches are developed by employing the semantic information in the so-called propagation link analysis method and in the language model, respectively. Experimental results on Yahoo! Answers dataset show that the extracted semantic information indeed improves the performance of both propagation and language model for the task of answer experts finding.
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