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Is Your Anchor Going Up or Down? Fast and Accurate Supervised Topic Models
Thang Nguyen
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Jordan Boyd-Graber
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Jeffrey Lund
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Kevin Seppi
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Eric Ringger
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Paper Details:
Month: May–June
Year: 2015
Location: Denver, Colorado
Venue:
NAACL |
Citations
URL
Efficient Methods for Incorporating Knowledge into Topic Models
Yi Yang
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Doug Downey
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Jordan Boyd-Graber
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Diversity in Spectral Learning for Natural Language Parsing
Shashi Narayan
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Shay B. Cohen
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Semi-Supervised Learning of Sequence Models with Method of Moments
Zita Marinho
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André F. T. Martins
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Shay B. Cohen
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Noah A. Smith
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Labeled Anchors and a Scalable, Transparent, and Interactive Classifier
Jeffrey Lund
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Stephen Cowley
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Wilson Fearn
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Emily Hales
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Kevin Seppi
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Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations
Zhe Zhang
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Munindar Singh
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Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112th Congress
Viet-An Nguyen
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Jordan Boyd-Graber
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Philip Resnik
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Kristina Miler
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ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling
Forough Poursabzi-Sangdeh
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Jordan Boyd-Graber
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Leah Findlater
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Kevin Seppi
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Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling
Jeffrey Lund
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Connor Cook
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Kevin Seppi
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Jordan Boyd-Graber
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Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge
Ryan J. Gallagher
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Kyle Reing
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David Kale
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Greg Ver Steeg
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The University of Maryland CLPsych 2015 Shared Task System
Philip Resnik
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William Armstrong
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Leonardo Claudino
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Thang Nguyen
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http://www.cs.princeton.edu/
http://scikit-learn.org/stable/
http://hunch.net/
http://mallet.cs.umass.edu/topics.php
Field Of Study
Linguistic Trends
Embeddings
Task
Sentiment Analysis
Approach
Topic Modeling
Language
Multilingual
French
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