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A: Expertise modeling for matching papers with reviewers
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| Venue: | Proceedings of the 13th ACM SIGKDD |
| Citations: | 54 - 2 self |
BibTeX
@INPROCEEDINGS{Mimno_a:expertise,
author = {David Mimno and Andrew Mccallum},
title = {A: Expertise modeling for matching papers with reviewers},
booktitle = {Proceedings of the 13th ACM SIGKDD},
year = {}
}
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Abstract
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. 1.
Keyphrases
expertise modeling novel topic model prospective reviewer author mention previous paper expert-finding task human relevance judgment language model unseen document hidden topic independent distribution several measure research paper rexa database apt topic model proposed reviewer essential part
