Exploring Topic Coherence over Many Models and Many Topics

By Keith Stevens, W. Philip Kegelmeyer Jr., David Andrzejewski, David J. Buttler. Conference on Empirical Methods in Natural Language Processing, 2012

573 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a6f793c53b1d1f09dbc11929bafc688b3e64d6bc

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Citations in the corpus

Machine learning and soil sciences: a review aided by machine learning tools (2019, background): “That balance between generality and specificity is key to generate topics that are semantically interpretable by a human (Stevens et al., 2012).”
Exploring the Space of Topic Coherence Measures (2015, background): “Subsequent empirical works on topic coherence [12, 18, 10] proposed a number of measures based on word statistics that differ in several details: definition, normalization and aggregation of word statistics and reference corpus.”
An analysis of the coherence of descriptors in topic modeling (2015, methodology): “At this point, we note that other work that measured coherence of topics found by LDA is largely focused upon the LDA u topic term descriptors of option 2 (Newman et al., 2010; Mimno et al., 2011; Stevens et al., 2012; Aletras & Stevenson, 2013; Lau et al., 2014).”
Improving Topic Models with Latent Feature Word Representations (2015, background): “The topic coherence evaluation measures to what extent the high-probability words in each topic are semantically coherent (Chang et al., 2009; Stevens et al., 2012). where the probabilities in equation (12) are derived from a 10-word sliding window over an external corpus.”
Full-Text or Abstract? Examining Topic Coherence Scores Using Latent Dirichlet Allocation (2017, unknown): “[20].”
SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions (2016, methodology): “For the topic-related objective, we take the advantage of the NMF Topic Model (Stevens et al., 2012), which is both simple and effective.”
Frontier Knowledge and Scientific Production: Evidence from the Collapse of International Science (2018, methodology): “18LSA also outperforms other machine-learning techniques such as Latent Dirichlet Allocation (Blei et al., 2003) or Non-negative Matrix Factorization (Lee and Seung, 2001) in word-similarity tasks (Stevens et al., 2012).”