TY - GEN
T1 - Detecting topic evolution in scientific literature
T2 - ACM 18th International Conference on Information and Knowledge Management, CIKM 2009
AU - He, Qi
AU - Chen, Bi
AU - Pei, Jian
AU - Qiu, Baojun
AU - Mitra, Prasenjit
AU - Giles, Lee
N1 - Funding Information:
Parts of this article were presented as "The Joys and Pains of Globalisation: The Case of Costa Rica" at the Australian Sociological Association Conference, Wollongong, I I December 1997. Previous work on the Thai shrimp sector developed in conjunction with David Butch is gratefully acknowledged. We would like to thank Tony van Fossen for helpful comments on an earlier draft of this article. We also appreciate a number of suggestions made by Philip McMichael and editorial assistance from Sue Goopy. Funding for part of this research was provided by a grant from the Australian Research Council. All translations and opinions expressed herein are solely the responsibility of the authors.
PY - 2009
Y1 - 2009
N2 - Understanding how topics in scientific literature evolve is an interesting and important problem. Previous work simply models each paper as a bag of words and also considers the impact of authors. However, the impact of one document on another as captured by citations, one important inherent element in scientific literature, has not been considered. In this paper, we address the problem of understanding topic evolution by leveraging citations, and develop citation-aware approaches. We propose an iterative topic evolution learning framework by adapting the Latent Dirichlet Allocation model to the citation network and develop a novel inheritance topic model. We evaluate the effectiveness and efficiency of our approaches and compare with the state of the art approaches on a large collection of more than 650,000 research papers in the last 16 years and the citation network enabled by CiteSeerX. The results clearly show that citations can help to understand topic evolution better.
AB - Understanding how topics in scientific literature evolve is an interesting and important problem. Previous work simply models each paper as a bag of words and also considers the impact of authors. However, the impact of one document on another as captured by citations, one important inherent element in scientific literature, has not been considered. In this paper, we address the problem of understanding topic evolution by leveraging citations, and develop citation-aware approaches. We propose an iterative topic evolution learning framework by adapting the Latent Dirichlet Allocation model to the citation network and develop a novel inheritance topic model. We evaluate the effectiveness and efficiency of our approaches and compare with the state of the art approaches on a large collection of more than 650,000 research papers in the last 16 years and the citation network enabled by CiteSeerX. The results clearly show that citations can help to understand topic evolution better.
UR - https://www.scopus.com/pages/publications/74549121414
UR - https://www.scopus.com/pages/publications/74549121414#tab=citedBy
U2 - 10.1145/1645953.1646076
DO - 10.1145/1645953.1646076
M3 - Conference contribution
AN - SCOPUS:74549121414
SN - 9781605585123
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 957
EP - 966
BT - ACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Y2 - 2 November 2009 through 6 November 2009
ER -