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Conference Paper: A collective topic model for milestone paper discovery

TitleA collective topic model for milestone paper discovery
Authors
KeywordsTopic Model
Milestone Paper
Paper Importance
Issue Date2014
PublisherACM.
Citation
The 37th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR 2014), Gold Coast, Australia, 6-11 July 2014. In Conference Proceedings, 2014, p. 1019-1022 How to Cite?
AbstractPrior arts stay at the foundation for future work in academic research. However the increasingly large amount of publications make it difficult for researchers to effectively discover the most important previous works to the topic of their research. In this paper, we study the automatic discovery of the core papers for a research area. We propose a collective topic model on three types of objects: papers, authors and published venues. We model any of these objects as bags of citations. Based on Probabilistic latent semantic analysis (PLSA), authorship, published venues and citation relations are used for quantifying paper importance. Our method discusses milestone paper discovery in different cases of input objects. Experiments on the ACL Anthology Network (ANN) indicate that our model is superior in mile-stone paper discovery when compared to a previous model which considers only papers.
Persistent Identifierhttp://hdl.handle.net/10722/198923
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLu, Z-
dc.contributor.authorMamoulis, N-
dc.contributor.authorCheung, DW-
dc.date.accessioned2014-07-18T03:06:55Z-
dc.date.available2014-07-18T03:06:55Z-
dc.date.issued2014-
dc.identifier.citationThe 37th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR 2014), Gold Coast, Australia, 6-11 July 2014. In Conference Proceedings, 2014, p. 1019-1022-
dc.identifier.isbn978-1-4503-2257-7-
dc.identifier.urihttp://hdl.handle.net/10722/198923-
dc.description.abstractPrior arts stay at the foundation for future work in academic research. However the increasingly large amount of publications make it difficult for researchers to effectively discover the most important previous works to the topic of their research. In this paper, we study the automatic discovery of the core papers for a research area. We propose a collective topic model on three types of objects: papers, authors and published venues. We model any of these objects as bags of citations. Based on Probabilistic latent semantic analysis (PLSA), authorship, published venues and citation relations are used for quantifying paper importance. Our method discusses milestone paper discovery in different cases of input objects. Experiments on the ACL Anthology Network (ANN) indicate that our model is superior in mile-stone paper discovery when compared to a previous model which considers only papers.-
dc.languageeng-
dc.publisherACM.-
dc.relation.ispartofProceedings of the 37th international ACM SIGIR Conference on Research and Development in Information Retrieval-
dc.subjectTopic Model-
dc.subjectMilestone Paper-
dc.subjectPaper Importance-
dc.titleA collective topic model for milestone paper discoveryen_US
dc.typeConference_Paperen_US
dc.identifier.emailMamoulis, N: nikos@cs.hku.hk-
dc.identifier.emailCheung, DW: dcheung@cs.hku.hk-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1145/2600428.2609499-
dc.identifier.hkuros230459-
dc.identifier.spage1019-
dc.identifier.epage1022-
dc.publisher.placeUnited States-

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