Combining distance and sequential dependencies in ezpert finding
Expert finding is the task of identifying persons with expertise on a given topic. Existing methods try to model the dependencies between candidates and terms with distance measure or sequential measure, which have been proven to be effective. However, to the best of our knowledge, no work has been conducted on the combination of the two dependencies. In this paper, we propose a language model based method to combine both dependencies under a unified framework. Specifically, we first propose an order kernel based document representation for incorporating the sequential dependency, and then we combine it with the proximity kernel based document representation which is designed to model the distance dependency. Our experiment results demonstrate the effectiveness of the order kernel and show that a linear combination of both dependencies can improve the performance significantly over the baseline method.
ezpert finding dependency distance sequence language model
Liu Yang Wensheng Zhang
The Key Lab of Complex Systems & Intelligence Science Institute of Automation,Chinese Academy of Science Beijing,China
国际会议
上海
英文
2306-2310
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）