Utilizing Context in Generative Bayesian Models for Linked Corpus

Saurabh Kataria, Prasenjit Mitra, Sumit Bhatia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

In an interlinked corpus of documents, the context in which a citation appears provides extra information about the cited document. However, associating terms in the context to the cited document remains an open problem. We propose a novel document generation approach that statistically incorporates the context in which a document links to another document. We quantitatively show that the proposed generation scheme explains the linking phenomenon better than previous approaches. The context information along with the actual content of the document provides signicant improvements over the previous approaches for various real world evaluation tasks such as link prediction and log-likelihood estimation on unseen content. The proposed method is more scalable to large collection of documents compared to the previous approaches.

Original languageEnglish (US)
Title of host publicationProceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010
PublisherAAAI press
Pages1340-1345
Number of pages6
ISBN (Electronic)9781577354642
StatePublished - Jul 15 2010
Event24th AAAI Conference on Artificial Intelligence, AAAI 2010 - Atlanta, United States
Duration: Jul 11 2010Jul 15 2010

Publication series

NameProceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010

Conference

Conference24th AAAI Conference on Artificial Intelligence, AAAI 2010
Country/TerritoryUnited States
CityAtlanta
Period7/11/107/15/10

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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