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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Citation Recommendation: Approaches and Datasets
Michael Färber,Adam Jatowt +1 more
TL;DR: This article presents an overview of the approaches and data sets for citation recommendation and identifies differences and commonalities using various dimensions, and sheds light on the evaluation methods and outline general challenges in the evaluation and how to meet them.
Journal ArticleDOI
Early detection of research trends
TL;DR: This study indicates that the emergence of a new topic is anticipated by a significant increase in the pace of collaboration between relevant research areas, which can be seen as the 'ancestors' of the new topic.
Proceedings Article
Modeling Topic-Level Academic Influence in Scientific Literatures.
TL;DR: J-Index, a quantitative metric modeling topic-level academic influence in scientific literatures, is introduced and it is shown how to learn RefTM to discover both the novelty of each paper and the strength of each citation.
A Bibliometric Study on Learning Analytics
TL;DR: Citation analysis was used to classify the knowledge structures and create a taxonomy of the 2730 research documents identified in the dataset to create the clustering analysis.
Proceedings ArticleDOI
EMNLP versus ACL: Analyzing NLP research over time
Sujatha Das Gollapalli,Xiaoli Li +1 more
TL;DR: A study on the research papers of approximately two decades from these two NLP conferences is presented and probabilistic and vector-based representations to represent the topics published in a venue for a given year are proposed.
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.