Proceedings ArticleDOI
Detecting topic evolution in scientific literature: how can citations help?
Qi He,Bi Chen,Jian Pei,Baojun Qiu,Prasenjit Mitra,C. Lee Giles +5 more
- pp 957-966
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TLDR
An iterative topic evolution learning framework is proposed by adapting the Latent Dirichlet Allocation model to the citation network and develop a novel inheritance topic model, which clearly shows that citations can help to understand topic evolution better.Abstract:
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.read more
Citations
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Proceedings ArticleDOI
Identification and Prediction of Emerging Topics through Their Relationships to Existing Topics
TL;DR: In this article, the authors proposed a novel topic evolution method for identifying and predicting the emergence of new topics under the assumption that neighborhoods of topics in the future have distinguishable structural features.
Proceedings ArticleDOI
Theme chronicle model: chronicle consists of timestamp and topical words over each theme
TL;DR: Experiments show that the proposed theme chronicle model (TCM) is useful as a generative model to discover fine-grained tightly coherent topics, takes advantage of previous models, and then assigns values for new documents.
Journal ArticleDOI
A Markov-switching approach to the study of citations in academic journals
TL;DR: A Markovian approach to study the stability and growth of citations in academic journals by featuring a regime-switching analysis shows that for most of the journals studied, the series of citations exhibit a stable growth.
Journal ArticleDOI
Citation context-based topic models: discovering cited and citing topics from full text
TL;DR: CC-LDA is effective in discovering the cited topics and citing topics with topic influence, and CCRef-Lda is able to find the cited topic influential references, according to the results.
Book ChapterDOI
LSA-PTM: a propagation-based topic model using latent semantic analysis on heterogeneous information networks
TL;DR: A topic propagation method is introduced that propagates the topics obtained by LSA on the heterogeneous information network via the links between different objects, which can optimize the topics and identify clusters of multi-typed objects simultaneously.
References
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Proceedings Article
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Term Weighting Approaches in Automatic Text Retrieval
Gerard Salton,Chris Buckley +1 more
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Journal ArticleDOI
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Journal ArticleDOI
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