The goal of expert-finding is to retrieve a ranked list of people as a response to a user query. Some models that proved to be very successful used the idea of association discovery in a window of text rather than the whole document. So far, all these studies only considered fixed window sizes. We propose an adaptive window-size approach for expert-finding. For this work we use some of the document attributes, such as document length, average sentence length, and number of candidates, to adjust the window size for the document. The experimental results indicate that taking document features into consideration when determining the window size, does have an effect on the retrieval outcome. The results shows an improvement over a range of baseline approaches
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