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» Unsupervised Natural Language Processing Using Graph Models
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COLING
2002
13 years 7 months ago
A Graph Model for Unsupervised Lexical Acquisition
This paper presents an unsupervised method for assembling semantic knowledge from a part-ofspeech tagged corpus using graph algorithms. The graph model is built by linking pairs o...
Dominic Widdows, Beate Dorow
ICASSP
2011
IEEE
12 years 11 months ago
Unsupervised determination of efficient Korean LVCSR units using a Bayesian Dirichlet process model
Korean is an agglutinative language that does not have explicit word boundaries. It is also a highly inflective language that exhibits severe coarticulation effects. These charac...
Sakriani Sakti, Andrew M. Finch, Ryosuke Isotani, ...
EMNLP
2010
13 years 5 months ago
On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing
This paper introduces dual decomposition as a framework for deriving inference algorithms for NLP problems. The approach relies on standard dynamic-programming algorithms as oracl...
Alexander M. Rush, David Sontag, Michael Collins, ...
ICASSP
2008
IEEE
14 years 2 months ago
Unsupervised language model adaptation via topic modeling based on named entity hypotheses
Language model (LM) adaptation is often achieved by combining a generic LM with a topic-specific model that is more relevant to the target document. Unlike previous work on unsup...
Yang Liu, Feifan Liu
CVPR
2007
IEEE
14 years 9 months ago
Unsupervised Activity Perception by Hierarchical Bayesian Models
We propose a novel unsupervised learning framework for activity perception. To understand activities in complicated scenes from visual data, we propose a hierarchical Bayesian mod...
Xiaogang Wang, Xiaoxu Ma, Eric Grimson