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1
Feature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields ...
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2
Controlling Complexity in Part-of-Speech Induction
In: Departmental Papers (CIS) (2011)
Abstract: We consider the problem of fully unsupervised learning of grammatical (part-of-speech) categories from unlabeled text. The standard maximum-likelihood hidden Markov model for this task performs poorly, because of its weak inductive bias and large model capacity. We address this problem by refining the model and modifying the learning objective to control its capacity via parametric and non-parametric constraints. Our approach enforces word-category association sparsity, adds morphological and orthographic features, and eliminates hard-to-estimate parameters for rare words. We develop an efficient learning algorithm that is not much more computationally intensive than standard training. We also provide an open-source implementation of the algorithm. Our experiments on five diverse languages (Bulgarian, Danish, English, Portuguese, Spanish) achieve significant improvements compared with previous methods for the same task.
Keyword: Computer Sciences
URL: https://repository.upenn.edu/cis_papers/493
https://repository.upenn.edu/cgi/viewcontent.cgi?article=1531&context=cis_papers
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3
Posterior Regularization for Learning with Side Information and Weak Supervision
In: Publicly Accessible Penn Dissertations (2010)
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4
Posterior regularization for learning with side information and weak supervision
In: Dissertations available from ProQuest (2010)
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5
Learning Tractable Word Alignment Models with Complex Constraints
In: Lab Papers (GRASP) (2010)
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6
PostCat - posterior constrained alignment toolkit
In: The Prague bulletin of mathematical linguistics. - Praha : Univ. (2009) 91, 27-36
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7
Dependency Grammar Induction via Bitext Projection Constraints
In: Lab Papers (GRASP) (2009)
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8
Penn/Umass/CHOP Biocreative II systems
In: Andrew McCallum (2007)
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