TY - CONF TI - Labeled LDA: A Supervised Topic Model for Credit Attribution in Multi-labeled Corpora AU - Ramage, Daniel AU - Hall, David AU - Nallapati, Ramesh AU - Manning, Christopher D. T3 - EMNLP '09 AB - A significant portion of the world's text is tagged by readers on social bookmarking websites. Credit attribution is an inherent problem in these corpora because most pages have multiple tags, but the tags do not always apply with equal specificity across the whole document. Solving the credit attribution problem requires associating each word in a document with the most appropriate tags and vice versa. This paper introduces Labeled LDA, a topic model that constrains Latent Dirichlet Allocation by defining a one-to-one correspondence between LDA's latent topics and user tags. This allows Labeled LDA to directly learn word-tag correspondences. The authors demonstrate Labeled LDA's improved expressiveness over traditional LDA with visualizations of a corpus of tagged web pages from del.icio.us. C1 - Stroudsburg, PA, USA C3 - Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1 DA - 2009/// PY - 2009 SP - 248 EP - 256 LA - en PB - Association for Computational Linguistics SN - 978-1-932432-59-6 ST - Labeled LDA UR - http://dl.acm.org/citation.cfm?id=1699510.1699543 Y2 - 2019/01/13/00:00:00 KW - Topic model algorithm KW - Topic model applied KW - Topic model labeling KW - Topic modeling ER -