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Automatically Creating Reading Lists with Topical PageRank

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If you have a question about this talk, please contact Ekaterina Kochmar.

We present an algorithm for creating reading lists – lists of papers given by an expert to a novice, designed to bring the novice up to speed in a certain area. Our algorithm uses a variant of PageRank that is age-corrected and sensitive to the mixture of papers’ topics as determined by the LDA topic model. When compared to a gold standard of reading lists which we collected from experts, our algorithm outperforms three currently used keyword-based search engines: Lucene, Google Scholar and the Google-indexed ACL Anthology. As evaluation metrics we use F-measure, as well as a new evaluation metric specific to reading lists which we introduce here. It estimates the degree of substitutability of expert papers by system-found ones by the number of links in the citation network between them. We also evaluate on the task of reference list reintroduction. When reintroducing the reference list of thousands of papers, our unsupervised algorithm performs on a par with the current state-of-the-art method, which is supervised.

This talk is part of the NLIP Seminar Series series.

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