Reading tea leaves: How humans interpret topic models

Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Volumn , Issue , 2009, Pages 288-296

  Chang, Jonathan ^a   Boyd Graber, Jordan ^b   Gerrish, Sean ^c   Wang, Chong ^c   Blei, David M ^c  

^a FACEBOOK   (United States)

^b UNIVERSITY OF MARYLAND   (United States)

^c Princeton University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

STATISTICS;

LARGE-SCALES; MODELING QUALITY; PREDICTIVE MODELS; PROBABILISTIC TOPIC MODELS; QUANTITATIVE EVALUATION; QUANTITATIVE METHOD; TEA-LEAVES; TOPIC MODELING; UNSUPERVISED ANALYSIS; USER STUDY;

SEMANTICS;

EID: 84863381525     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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