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53
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ACL
2010
15 years 18 days ago
Word Representations: A Simple and General Method for Semi-Supervised Learning
Joseph P. Turian, Lev-Arie Ratinov, Yoshua Bengio
JMLR
2010
153views more  JMLR 2010»
14 years 9 months ago
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
In this paper, we present an overview of generalized expectation criteria (GE), a simple, robust, scalable method for semi-supervised training using weakly-labeled data. GE fits m...
Gideon S. Mann, Andrew McCallum
140
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IJSI
2008
156views more  IJSI 2008»
15 years 2 months ago
Co-Training by Committee: A Generalized Framework for Semi-Supervised Learning with Committees
Many data mining applications have a large amount of data but labeling data is often difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supe...
Mohamed Farouk Abdel Hady, Friedhelm Schwenker
121
Voted
NAACL
2007
15 years 4 months ago
Data-Driven Graph Construction for Semi-Supervised Graph-Based Learning in NLP
Graph-based semi-supervised learning has recently emerged as a promising approach to data-sparse learning problems in natural language processing. All graph-based algorithms rely ...
Andrei Alexandrescu, Katrin Kirchhoff
201
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EMNLP
2011
14 years 2 months ago
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions
We introduce a novel machine learning framework based on recursive autoencoders for sentence-level prediction of sentiment label distributions. Our method learns vector space repr...
Richard Socher, Jeffrey Pennington, Eric H. Huang,...