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COLING
2010
13 years 4 months ago
Generative Alignment and Semantic Parsing for Learning from Ambiguous Supervision
We present a probabilistic generative model for learning semantic parsers from ambiguous supervision. Our approach learns from natural language sentences paired with world states ...
Joohyun Kim, Raymond J. Mooney
FLAIRS
2003
13 years 11 months ago
Orthographic Case Restoration Using Supervised Learning Without Manual Annotation
One challenge in text processing is the treatment of case insensitive documents such as speech recognition results. The traditional approach is to re-train a language model exclud...
Cheng Niu, Wei Li 0003, Jihong Ding, Rohini K. Sri...
ICASSP
2010
IEEE
13 years 10 months ago
Weakly supervised learning with decision trees applied to fisheries acoustics
This paper addresses the training of classification trees for weakly labelled data. We call ”weakly labelled data”, a training set such as the prior labelling information pro...
Riwal Lefort, Ronan Fablet, Jean-Marc Boucher
ICPR
2006
IEEE
14 years 11 months ago
A New Data Selection Principle for Semi-Supervised Incremental Learning
Current semi-supervised incremental learning approaches select unlabeled examples with predicted high confidence for model re-training. We show that for many applications this dat...
Alexander I. Rudnicky, Rong Zhang
NLDB
2007
Springer
14 years 4 months ago
Four Methods for Supervised Word Sense Disambiguation
Word sense disambiguation is the task to identify the intended meaning of an ambiguous word in a certain context, one of the central problems in natural language processing. This p...
Kinga Schumacher