• Publications
  • Influence
Finding scientific topics
TLDR
A first step in identifying the content of a document is determining which topics that document addresses. Expand
  • 4,876
  • 556
  • PDF
The Author-Topic Model for Authors and Documents
TLDR
We introduce the author-topic model, a generative model for documents that extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan) to include authorship information. Expand
  • 1,457
  • 130
  • PDF
The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth
TLDR
We present statistical analyses of the large-scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. Expand
  • 1,046
  • 119
  • PDF
Topics in semantic representation.
TLDR
This article analyzes the abstract computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference. Expand
  • 901
  • 106
  • PDF
A model for recognition memory: REM—retrieving effectively from memory
A new model of recognition memory is reported. This model is placed within, and introduces, a more elaborate theory that is being developed to predict the phenomena of explicit and implicit, andExpand
  • 844
  • 93
Probabilistic author-topic models for information discovery
TLDR
We propose a new unsupervised learning technique for extracting information from large text collections. Expand
  • 628
  • 62
  • PDF
Integrating Topics and Syntax
TLDR
We present a generative model for text in which a hidden Markov model determines when to emit a word from a topic model. Expand
  • 558
  • 40
  • PDF
Inferring causal networks from observations and interventions
TLDR
We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision-making. Expand
  • 394
  • 32
  • PDF
Learning author-topic models from text corpora
TLDR
We propose an unsupervised learning technique for extracting information about authors and topics from large text collections. Expand
  • 292
  • 32
  • PDF
Statistical topic models for multi-label document classification
TLDR
We investigate a class of generative statistical topic models for multi-label document classification that associate individual word tokens with different labels. Expand
  • 256
  • 30
  • PDF