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A systematic comparison of various statistical alignment models
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A statistical model for word discovery in transcribed speech
Computational Linguistics
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On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
Online large-margin training of dependency parsers
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Contextual dependencies in unsupervised word segmentation
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SVM optimization: inverse dependence on training set size
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Distributed asynchronous online learning for natural language processing
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Crouching Dirichlet, hidden Markov model: unsupervised POS tagging with context local tag generation
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Unsupervised word alignment with arbitrary features
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
The CMU-ARK German-English translation system
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Simple semi-supervised learning for prepositional phrase attachment
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Regularized Latent Semantic Indexing: A New Approach to Large-Scale Topic Modeling
ACM Transactions on Information Systems (TOIS)
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The (batch) EM algorithm plays an important role in unsupervised induction, but it sometimes suffers from slow convergence. In this paper, we show that online variants (1) provide significant speedups and (2) can even find better solutions than those found by batch EM. We support these findings on four unsupervised tasks: part-of-speech tagging, document classification, word segmentation, and word alignment.