Mixed-membership models of scientific publications

By Elena A. Erosheva, Stephen E. Fienberg, John D. Lafferty. Proceedings of the National Academy of Sciences of the United States of America, 2004

536 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a823864e65d39d73b7107b6e64c4c06bb40a4244

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Citations in the corpus

Dynamic mixed membership blockmodel for evolving networks (2009, background): “Recently there is a surge of interest in applying latent space model for network modeling and analysis (Ho. et al., 2002; Li & McCallum, 2006; Handcock et al., 2007; Erosheva et al., 2004; Airoldi et al., 2008).”
Supervised Topic Models (2007, background): “The complexity of document corpora has led to considerable interest in applying hierarchical statistical models based on what are called topics (Blei and Lafferty, 2009; Blei et al., 2003; Griffiths et al., 2005; Erosheva et al., 2004).”
Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression (2008, methodology): “Examples of such “downstream” models include the authorship model of Erosheva, Fienberg and Laﬀerty [5], the Topics over Time (TOT) model of Wang and McCallum [15], the Group-Topic model of Wang, Mohanty and McCallum [16], the CorrLDA model of Blei and Jordan [1] and the named entity models of…”
Context-aware citation recommendation (2010, background): “Let d be a document, and D be a document corpus.”
Block-LDA: Jointly Modeling Entity-Annotated Text and Entity-Entity Links (2014, background): “Extensions to LDA [4, 5] additionally model other metadata in documents such as authors and entities, by treating a latent topic as a set of distributions, one for each metadata type.”
A correlated topic model of Science (2007, methodology): “Recent work has used LDA as a building block in more sophisticated topic models, such as author-document models [19, 24], abstract-reference models [12] syntaxsemantics models [16] and image-caption models [6].”
Open domain event extraction from twitter (2012, methodology): “The generative story for our data is based on LinkLDA [15], and is presented as Algorithm 1.”
Center-piece subgraphs: problem definition and fast solutions (2006, methodology): “To study CePS from the point view of generative model, such as Kronecker graph [23], mixed-membership model [2], infinite relational model [21] etc.”
A Survey of Statistical Network Models (2009, background): “The class of mixed membership models resembles a form of soft clustering [95] and includes the latent Dirichlet allocation model [41] from machine learning as a special case.”
Natural Language Models for Predicting Programming Comments (2013, methodology): “We hence also evaluate the use of LDA (Blei et al., 2003) and link-LDA (Erosheva et al., 2004) topic models, which are more relevant for the term extraction scenario.”
Reading Tea Leaves: How Humans Interpret Topic Models (2009, unknown): “In this work we study three topic models: probabilistic latent semantic indexing (pLSI) [3], latent Dirichlet allocation (LDA) [4], and the correlated topic model (CTM) [5], which are all mixed membership models [17].”
The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling (2010, methodology): “Mixed membership models are an effective tool for capturing complex data heterogeneity (Erosheva et al., 2004).”
Research-paper recommender systems: a literature survey (2015, methodology): “The approaches extracted words from the title [79,85, 112], abstract [26,51,61], header [43], introduction [57], foreword [57], author-provided keywords [26,57,58], and bibliography [27], as well as from the papers’ body text [65,101,112].”