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Stochastic And-Or grammars compactly represent both compositionality and re- configurability and have been used to model different types of data such as ...
We present a unified formalization of stochastic And-Or grammars that is agnostic to the type of the data being modeled, and propose an unsupervised approach to ...
Stochastic And-Or grammars compactly represent both compositionality and re- configurability and have been used to model different types of data such as ...
Unsupervised Structure Learning of Stochastic And-Or Grammars
faculty.sist.shanghaitech.edu.cn › tukw
Dec 25, 2013 · Stochastic And-Or grammars compactly represent both compositionality and reconfigurability and have been used to model different types of data ...
In this paper, we proposed an unsupervised And-. Or grammar learning approach that iteratively searches for better grammar structure and pa- rameters to ...
In this section, we derive the likelihood gain of adding an And-Or fragment into the grammar. In our learning algorithm when an And-Or fragment is added ...
We present a unified formalization of stochastic And-Or grammars that is agnostic to the type of the data being modeled, and propose an unsupervised approach to ...
An algorithm is presented for learning a phrase-structure grammar from tagged text. It clusters sequences of tags to- gether based on local distributional ...
▫ Unsupervised grammar learning can shed light on language acquisition and linguistic structure. Page 13. Is this the right problem? ▫ Before proceeding…are ...
Formally, HCMs can be represented by hierarchical graph structures. • Relations to Deep Networks. • The parts/subparts and relationships between.