Expertise modeling for matching papers with reviewers

author: David Mimno, Department of Computer Science, University of Massachusetts Amherst
published: Sept. 14, 2007,   recorded: September 2007,   views: 3186

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


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 a document to be reviewed 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 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.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: