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ICASSP
2011
IEEE
12 years 9 months ago
Toward text message normalization: Modeling abbreviation generation
This paper describes a text normalization system for deletion-based abbreviations in informal text. We propose using statistical classifiers to learn the probability of deleting ...
Deana Pennell, Yang Liu
NIPS
2008
13 years 6 months ago
Unsupervised Learning of Visual Sense Models for Polysemous Words
Polysemy is a problem for methods that exploit image search engines to build object category models. Existing unsupervised approaches do not take word sense into consideration. We...
Kate Saenko, Trevor Darrell
CVPR
2007
IEEE
14 years 7 months ago
Learning Visual Representations using Images with Captions
Current methods for learning visual categories work well when a large amount of labeled data is available, but can run into severe difficulties when the number of labeled examples...
Ariadna Quattoni, Michael Collins, Trevor Darrell
JMLR
2012
11 years 7 months ago
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing
Open-text semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR – a formal representation of its s...
Antoine Bordes, Xavier Glorot, Jason Weston, Yoshu...
HRI
2006
ACM
13 years 11 months ago
Using context and sensory data to learn first and second person pronouns
We present a method of grounded word learning that is powerful enough to learn the meanings of first and second person pronouns. The model uses the understood words in an utteran...
Kevin Gold, Brian Scassellati