Automatic labeling of multinomial topic models

By Qiaozhu Mei, Xuehua Shen, Chengxiang Zhai. Knowledge Discovery and Data Mining, 2007

421 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/8d772e289f7ddba8a6ace8e63da1f67ae814b099

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Citations in the corpus

How to Analyze Political Attention with Minimal Assumptions and Costs (2010, methodology): “In fact, Mei et al. (2007) recommend an automated labeling scheme for such models, essentially listing these major keywords as the label.”
Reading Tea Leaves: How Humans Interpret Topic Models (2009, background): “This implicit notion that topics have semantic meaning for users has even motivated work that attempts to automatically label topics [16].”
Mining Text Data (2012, methodology): “A number of similar methods along this spirit are proposed in [4, 14, 35, 47, 89] with varying levels of supervision in the clustering process.”
Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures (2014, background): “There exist some automatic labeling methods (Mei et al. 2007), but these are not suitable in cases where such labeling requires domain knowledge (financial knowledge in our case).”
Automatic Evaluation of Topic Coherence (2010, background): “Some researchers have started to address this problem, including Mei et al. (2007) who presented approaches for automatic labeling of topics (which is core to the question of coherence and semantic interpretability), and Grifﬁths and Steyvers (2006) who applied topic models to word sense…”
Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora (2009, background): “Labeled LDA’s topics are directly named with the tag that corresponds to each topic, an improvement over standard practice of inferring the topic name by inspection (Mei et al., 2007).”
A Model of Text for Experimentation in the Social Sciences (2016, methodology): “Themajority of topic models are summarized by the most frequent terms within a topic, although there are several methods for choosing higher order phrases (Mei, Shen, and Zhai 2007; Blei and Lafferty 2009).”
Software traceability with topic modeling (2010, background): “work that seeks to provide topic labels automatically [31].”
What are developers talking about? An analysis of topics and trends in Stack Overflow (2014, methodology): “We also note that automated methods have recently been proposed to assign labels to topics (Mei et al. 2007).”