Stochastic Attribute-Value Grammars

Stochastic Attribute-Value Grammars (1997)

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by Steven P. Abney

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119 - 0 self

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@MISC{Abney97stochasticattribute-value,
    author = {Steven P. Abney},
    title = {Stochastic Attribute-Value Grammars},
    year = {1997}
}

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Abstract

Citations

464	Inducing Features of Random Fields - Pietra, Pietra, et al. - 1997
173	Image Analysis, Random Fields and Dynamic Monte Carlo Methods - Winkler - 1995
116	Elementary Principles of Statistical Mechanics - Gibbs - 1902
29	Probabilistic constraint logic programming - RIEZLER - 1998
27	Stochastic HPSG - Brew - 1995
12	Towards probabilistic extensions of constraintbased grammars - Eisele - 1994
12	Parameter estimation for constrained context-free language models - Mark, Miller, et al. - 1992
8	Quantitative constraint logic programming for weighted grammar applications. Talk given at LACL - Riezler - 1996
1	Quantitative constraint logic programming for Abney Stochastic Attribute-Value Grammars weighted grammar applications. Talk given at LACL - Riezler - 1996
1	Weight Estimation In the feature selection step, we choose an initial weight fi for each candidate feature f so as to maximize the gain G = D(~pjjq old ) \Gamma D(~pjjq f;fi ) of adding f to the field. It is actually more convenient to consider log weight - Initial - 1995
1	can re-express this expectation in terms of the old field q old : q ff [f r ] = P x f r (x)q ff (x) = P x f r (x)e fff(x) q old (x) P x e fff(x) q old (x) = q old [f r e fff ] q old [e fff ] The expectations q old [f r e fff ] can be obtained by generatin - We - 1995
1	Entropies, combinatorics and probabilities of context-free branching processes - Miller, O'Sullivan - 1990
1	αt) we require ˜p[f] and qα[f], and F ′ (αt) can be expressed as qα[f] 2 −qα[f 2 ]. ˜p[f] is simply the average value of f in the training corpus. The remaining terms are all of the form qα[f r ]. We can re-express this expectation in terms of the old fie - For - 1995