Author name cluster

Shay Cohen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

AAAI Conference 2018 Conference Paper

Canonical Correlation Inference for Mapping Abstract Scenes to Text

Nikos Papasarantopoulos
Helen Jiang
Shay Cohen

We describe a technique for structured prediction, based on canonical correlation analysis. Our learning algorithm ﬁnds two projections for the input and the output spaces that aim at projecting a given input and its correct output into points close to each other. We demonstrate our technique on a language-vision problem, namely the problem of giving a textual description to an “abstract scene. ”

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NeurIPS Conference 2012 Conference Paper

Tensor Decomposition for Fast Parsing with Latent-Variable PCFGs

Michael Collins
Shay Cohen

We describe an approach to speed-up inference with latent variable PCFGs, which have been shown to be highly effective for natural language parsing. Our approach is based on a tensor formulation recently introduced for spectral estimation of latent-variable PCFGs coupled with a tensor decomposition algorithm well-known in the multilinear algebra literature. We also describe an error bound for this approximation, which bounds the difference between the probabilities calculated by the algorithm and the true probabilities that the approximated model gives. Empirical evaluation on real-world natural language parsing data demonstrates a significant speed-up at minimal cost for parsing performance.

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NeurIPS Conference 2010 Conference Paper

Empirical Risk Minimization with Approximations of Probabilistic Grammars

Noah Smith
Shay Cohen

Probabilistic grammars are generative statistical models that are useful for compositional and sequential structures. We present a framework, reminiscent of structural risk minimization, for empirical risk minimization of the parameters of a fixed probabilistic grammar using the log-loss. We derive sample complexity bounds in this framework that apply both to the supervised setting and the unsupervised setting.

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NeurIPS Conference 2008 Conference Paper

Logistic Normal Priors for Unsupervised Probabilistic Grammar Induction

Shay Cohen
Kevin Gimpel
Noah Smith

We explore a new Bayesian model for probabilistic grammars, a family of distributions over discrete structures that includes hidden Markov models and probabilistic context-free grammars. Our model extends the correlated topic model framework to probabilistic grammars, exploiting the logistic normal distribution as a prior over the grammar parameters. We derive a variational EM algorithm for that model, and then experiment with the task of unsupervised grammar induction for natural language dependency parsing. We show that our model achieves superior results over previous models that use different priors.

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IJCAI Conference 2005 Conference Paper

Feature Selection Based on the Shapley Value

Shay Cohen
Eytan Ruppin
Gideon

We present and study the Contribution-Selectionalgorithm (CSA), a novel algorithm for feature selection. The algorithm is based on the Multiperturbation Shapley Analysis, a framework which relies on game theory to estimate usefulness. The algorithm iteratively estimates the usefulness of features and selects them accordingly, using either forward selection or backward elimination. Empirical comparison with several other existing feature selection methods shows that the backward elimination variant of CSA leads to the most accurate classification results on an array of datasets.

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