document: Expertise modeling for matching papers with reviewers An essential part of an expert-finding task , such as matching reviewers to submitted papers , is the ability to model the expertise of a person based on documents . We evaluate several measures of the association between an author in an existing collection of research papers and a previously unseen document . We compare two language model based approaches with a novel topic model , Author-Persona-Topic ( APT ) . In this model , each author can write under one or more `` personas , '' which are represented as independent distributions over hidden topics . Examples of previous papers written by prospective reviewers are gathered from the Rexa database , which extracts and disambiguates author mentions from documents gathered from the web . We evaluate the models using a reviewer matching task based on human relevance judgments determining how well the expertise of proposed reviewers matches a submission . We find that the APT topic model outperforms the other models .
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