A collective topic model for milestone paper discovery

Ziyu Lu, Nikos Mamoulis, David W. Cheung
2014 Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval - SIGIR '14  
Prior 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
more » ... 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 milestone paper discovery when compared to a previous model which considers only papers.
doi:10.1145/2600428.2609499 dblp:conf/sigir/LuMC14 fatcat:ag4cco2zbjc5nn2qwefikinz4i