Discriminative Learning for Probabilistic Context-Free Grammars based on Generalized H-Criterion
We present a formal framework for the development of a family of discriminative learning algorithms for Probabilistic Context-Free Grammars (PCFGs) based on a generalization of criterion-H. First of all, we propose the H-criterion as the objective function and the Growth Transformations as the optimization method, which allows us to develop the final expressions for the estimation of the parameters of the PCFGs. And second, we generalize the H-criterion to take into account the set of reference interpretations and the set of competing interpretations, and we propose a new family of objective functions that allow us to develop the expressions of the estimation transformations for PCFGs.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Advances in Using Grammars with Latent Annotations for Discontinuous Parsing
We present new experiments that transfer techniques from Probabilistic Context-free Grammars with Latent Annotations (PCFG-LA) to two grammar formalisms for discontinuous parsing: linear context-free rewriting systems an…
Learning of Structurally Unambiguous Probabilistic Grammars
The problem of identifying a probabilistic context free grammar has two aspects: the first is determining the grammar's topology (the rules of the grammar) and the second is estimating probabilistic weights for each rule…
Learning of Structurally Unambiguous Probabilistic Grammars
The problem of identifying a probabilistic context free grammar has two aspects: the first is determining the grammar's topology (the rules of the grammar) and the second is estimating probabilistic weights for each rule…
A generalized parsing framework for Abstract Grammars
This technical report presents a general framework for parsing a variety of grammar formalisms. We develop a grammar formalism, called an Abstract Grammar, which is general enough to represent grammars at many levels of …
Probabilistic Grammars for Equation Discovery
Equation discovery, also known as symbolic regression, is a type of automated modeling that discovers scientific laws, expressed in the form of equations, from observed data and expert knowledge. Deterministic grammars, …
Equation DiscoverySymbolic Regression