Compound probabilistic context-free grammars for grammar induction

compound probabilistic context-free grammars for grammar induction

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CL ; Machine Learning cs. Have an idea for a. Computer Vision and Pattern Recognition.

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Context-Free Grammars (CFG) and Context-Free Languages (CFL) - what are they?
Compound Probabilistic Context-Free Grammars. Code for the paper: Compound Probabilistic Context-Free Grammars for Grammar Induction Yoon Kim, Chris Dyer. Aiming at unifying all extensions of context-free grammars (XCFGs). X stands for weighted, (compound) probabilistic, and neural extensions, etc. Currently only. A Generative Constituent-Context Model for. Improved Grammar Induction. In Proceedings of ACL. Dan Klein and Christopher Manning. Corpus-based Induction.
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First, the distribution of tree depths at initialization should show a wide range, because at this time the tree depths are only correlated to sentence length. These non-nativist models Bannard, Lieven, and Tomasello usually assume that the grammar children first acquire is linear and templatic, consisting of multiword frames with slots to be filled in or just n -grams. Received: March 04 The code was tested in python 3. Much of this grammar induction work used strong linguistically motivated constraints or direct linguistic annotation to help the inducer eliminate some local optima.