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NAACL
2010
13 years 5 months ago
From Baby Steps to Leapfrog: How "Less is More" in Unsupervised Dependency Parsing
We present three approaches for unsupervised grammar induction that are sensitive to data complexity and apply them to Klein and Manning's Dependency Model with Valence. The ...
Valentin I. Spitkovsky, Hiyan Alshawi, Daniel Jura...
ACL
2004
13 years 8 months ago
Annealing Techniques For Unsupervised Statistical Language Learning
Exploiting unannotated natural language data is hard largely because unsupervised parameter estimation is hard. We describe deterministic annealing (Rose et al., 1990) as an appea...
Noah A. Smith, Jason Eisner
EMNLP
2011
12 years 7 months ago
Unsupervised Dependency Parsing without Gold Part-of-Speech Tags
We show that categories induced by unsupervised word clustering can surpass the performance of gold part-of-speech tags in dependency grammar induction. Unlike classic clustering ...
Valentin I. Spitkovsky, Hiyan Alshawi, Angel X. Ch...
ACL
2008
13 years 8 months ago
Semi-Supervised Convex Training for Dependency Parsing
We present a novel semi-supervised training algorithm for learning dependency parsers. By combining a supervised large margin loss with an unsupervised least squares loss, a discr...
Qin Iris Wang, Dale Schuurmans, Dekang Lin
NAACL
2001
13 years 8 months ago
Unsupervised Learning of Name Structure From Coreference Data
We present two methods for learning the structure of personal names from unlabeled data. The first simply uses a few implicit constraints governing this structure to gain a toehol...
Eugene Charniak