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EMNLP
2008
13 years 9 months ago
Modeling Annotators: A Generative Approach to Learning from Annotator Rationales
A human annotator can provide hints to a machine learner by highlighting contextual "rationales" for each of his or her annotations (Zaidan et al., 2007). How can one ex...
Omar Zaidan, Jason Eisner
IEICET
2008
136views more  IEICET 2008»
13 years 7 months ago
Bilingual Cluster Based Models for Statistical Machine Translation
We propose a domain specific model for statistical machine translation. It is wellknown that domain specific language models perform well in automatic speech recognition. We show ...
Hirofumi Yamamoto, Eiichiro Sumita
ML
2010
ACM
135views Machine Learning» more  ML 2010»
13 years 2 months ago
Multi-domain learning by confidence-weighted parameter combination
State-of-the-art statistical NLP systems for a variety of tasks learn from labeled training data that is often domain specific. However, there may be multiple domains or sources o...
Mark Dredze, Alex Kulesza, Koby Crammer
ICPR
2006
IEEE
14 years 8 months ago
Domain Based LDA and QDA
We propose an alternative to probability density classifiers based on normal distributions LDA and QDA. Instead of estimating covariance matrices using the standard maximum likeli...
David M. J. Tax, Piotr Juszczak, Robert P. W. Duin...
KDD
2004
ACM
196views Data Mining» more  KDD 2004»
14 years 7 months ago
Adversarial classification
Essentially all data mining algorithms assume that the datagenerating process is independent of the data miner's activities. However, in many domains, including spam detectio...
Nilesh N. Dalvi, Pedro Domingos, Mausam, Sumit K. ...