An essential part of an expert-nding task, such as matching
reviewers to submitted papers, is the ability to model the ex-
pertise of a person based on documents. We evaluate several
measures of the association between a document to be re-
viewed and an author, represented by their previous papers.
We compare 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 ex-
tracts and disambiguates author mentions from documents
gathered from the web. We evaluate the models using a re-
viewer matching task based on human relevance judgments
determining how well the expertise of proposed reviewers
matches a submission. We nd that the APT topic model
outperforms the other models.