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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Journal ArticleDOI
Quantitative Horizon Scanning for Mitigating Technological Surprise: Detecting the Potential for Collaboration at the Interface
TL;DR: This work develops an innovative statistical approach thereto—not a final etched‐in‐stone approach, but perhaps the first complete quantitative methodology explicitly addressing QHS for MTS.
Dissertation
Towards structured representation of academic search results
TL;DR: A novel method of representing academic search results with concise and informative topic maps, based on sequential prediction to automatically learn to build informative summaries from examples, and an interactive learning method for selecting the categories of Wikipedia relevant to a given domain.
Journal ArticleDOI
Refining the Measurement of Topic Similarities Through Bibliographic Coupling and LDA
TL;DR: This paper presents an approach for measuring the similarity between topics based on the bibliographic coupling and believes that finding such an association between unrelated innovative inventions across various industries may help public and private research units in planning research direction and serve as a reference for future research.
Journal ArticleDOI
Measuring the innovation of method knowledge elements in scientific literature
Journal ArticleDOI
MRT: Tracing the Evolution of Scientific Publications
TL;DR: This work proposed a practical framework called Master Reading Tree (MRT), which can build annotated evolution roadmaps for publications and identify important previous works or evolution tracks by generating expressive embeddings and clustering them into various groups.
References
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Journal ArticleDOI
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Proceedings Article
Latent Dirichlet Allocation
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Journal ArticleDOI
Term Weighting Approaches in Automatic Text Retrieval
Gerard Salton,Chris Buckley +1 more
TL;DR: This paper summarizes the insights gained in automatic term weighting, and provides baseline single term indexing models with which other more elaborate content analysis procedures can be compared.
Journal ArticleDOI
Finding scientific topics
TL;DR: A generative model for documents is described, introduced by Blei, Ng, and Jordan, and a Markov chain Monte Carlo algorithm is presented for inference in this model, which is used to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics.
Journal ArticleDOI
Hierarchical Dirichlet Processes
TL;DR: This work considers problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups, and considers a hierarchical model, specifically one in which the base measure for the childDirichlet processes is itself distributed according to a Dirichlet process.