In Search of Coherence and Consensus: Measuring the Interpretability of Statistical Topics
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[1] Kirill Kireyev. Applications of Topics Models to Analysis of Disaster-Related Twitter Data , 2009 .
[2] Robert M. Rolfe,et al. Exploratory analysis of highly heterogeneous document collections , 2013, KDD.
[3] Yee Whye Teh,et al. On Smoothing and Inference for Topic Models , 2009, UAI.
[4] Hady Wirawan Lauw,et al. Semantic Visualization with Neighborhood Graph Regularization , 2016, J. Artif. Intell. Res..
[5] Justin Grimmer,et al. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts , 2013, Political Analysis.
[6] F. Jelinek,et al. Perplexity—a measure of the difficulty of speech recognition tasks , 1977 .
[7] Thomas Hofmann,et al. Probabilistic latent semantic indexing , 1999, SIGIR '99.
[8] Huan Liu,et al. Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose , 2013, ICWSM.
[9] John D. Lafferty,et al. Correlated Topic Models , 2005, NIPS.
[10] Michael I. Jordan,et al. Hierarchical Dirichlet Processes , 2006 .
[11] James H. Martin,et al. Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd Edition , 2000, Prentice Hall series in artificial intelligence.
[12] Timothy Baldwin,et al. Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality , 2014, EACL.
[13] Alexander J. Smola,et al. Discovering geographical topics in the twitter stream , 2012, WWW.
[14] Jianhua Lin,et al. Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.
[15] Chun How Tan,et al. Beyond "local", "categories" and "friends": clustering foursquare users with latent "topics" , 2012, UbiComp.
[16] Timothy Baldwin,et al. Automatic Evaluation of Topic Coherence , 2010, NAACL.
[17] Gerlof Bouma,et al. Normalized (pointwise) mutual information in collocation extraction , 2009 .
[18] Reza Zafarani,et al. Whom should I follow?: identifying relevant users during crises , 2013, HT.
[19] David B. Dunson,et al. Probabilistic topic models , 2012, Commun. ACM.
[20] Fred Morstatter,et al. Finding Eyewitness Tweets During Crises , 2014, LTCSS@ACL.
[21] Ted Pedersen,et al. An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet , 2002, CICLing.
[22] Hongfei Yan,et al. Automatic labeling hierarchical topics , 2012, CIKM '12.
[23] Fei Wang,et al. ET-LDA: Joint Topic Modeling for Aligning Events and their Twitter Feedback , 2012, AAAI.
[24] Qiang Liu,et al. Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy , 2014, ICML.
[25] Timothy Baldwin,et al. Automatic Labelling of Topic Models , 2011, ACL.
[26] L. R. Rasmussen,et al. In information retrieval: data structures and algorithms , 1992 .
[27] Lillian Lee,et al. Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..
[28] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[29] Quentin Pleple,et al. Interactive Topic Modeling , 2013 .
[30] Daniel Barbará,et al. Topic Significance Ranking of LDA Generative Models , 2009, ECML/PKDD.
[31] A. Hirschman. National Power and the Structure of Foreign Trade , 2024 .
[32] Mark Stevenson,et al. Evaluating Topic Coherence Using Distributional Semantics , 2013, IWCS.
[33] Wei Li,et al. Pachinko allocation: DAG-structured mixture models of topic correlations , 2006, ICML.
[34] Chong Wang,et al. Reading Tea Leaves: How Humans Interpret Topic Models , 2009, NIPS.
[35] David W. Conrath,et al. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy , 1997, ROCLING/IJCLCLP.
[36] Isabell M. Welpe,et al. Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.
[37] Mark Stevenson,et al. Labelling Topics using Unsupervised Graph-based Methods , 2014, ACL.
[38] Andrew McCallum,et al. Optimizing Semantic Coherence in Topic Models , 2011, EMNLP.
[39] Brendan T. O'Connor,et al. A Latent Variable Model for Geographic Lexical Variation , 2010, EMNLP.
[40] Brendan T. O'Connor,et al. Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks , 2008, EMNLP.
[41] Ramesh Nallapati,et al. Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora , 2009, EMNLP.
[42] Jiawei Han,et al. Geographical topic discovery and comparison , 2011, WWW.
[43] Alexei Pozdnoukhov,et al. Space-time dynamics of topics in streaming text , 2011, LBSN '11.
[44] Hong Cheng,et al. The dual-sparse topic model: mining focused topics and focused terms in short text , 2014, WWW.
[45] Kenneth E. Shirley,et al. LDAvis: A method for visualizing and interpreting topics , 2014 .
[46] David C. Hoaglin,et al. Some Implementations of the Boxplot , 1989 .
[47] C. Spearman. The proof and measurement of association between two things. , 2015, International journal of epidemiology.
[48] Christopher D. Manning,et al. Topic Modeling for the Social Sciences , 2009 .
[49] H. Russell Bernard,et al. Analyzing Qualitative Data: Systematic Approaches , 2009 .
[50] Huan Liu,et al. Text, Topics, and Turkers: A Consensus Measure for Statistical Topics , 2015, HT.
[51] Michael Röder,et al. Exploring the Space of Topic Coherence Measures , 2015, WSDM.