M
Mark Steyvers
Researcher at University of California, Irvine
Publications - 167
Citations - 20010
Mark Steyvers is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Topic model & Cognition. The author has an hindex of 52, co-authored 152 publications receiving 18492 citations. Previous affiliations of Mark Steyvers include University of California & University of California, Berkeley.
Papers
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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.
Proceedings ArticleDOI
The author-topic model for authors and documents
TL;DR: The author-topic model is introduced, a generative model for documents that extends Latent Dirichlet Allocation to include authorship information, and applications to computing similarity between authors and entropy of author output are demonstrated.
Journal ArticleDOI
The large-scale structure of semantic networks: statistical analyses and a model of semantic growth.
TL;DR: A simple model for semantic growth is described, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node, which generates appropriate small-world statistics and power-law connectivity distributions.
Book ChapterDOI
Probabilistic Topic Models
TL;DR: Landauer and Dumais as discussed by the authors showed that applying a statistical method such as latent semantic analysis (LSA) to large databases can yield insight into human cognition, and proposed a class of statistical models in which the semantic properties of words and documents are expressed in terms of probabilistic topics.
Journal ArticleDOI
Topics in semantic representation.
TL;DR: This article analyzes the abstract computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference that leads to a novel approach to semantic representation in which word meanings are represented in terms of a set of probabilistic topics.