Probabilistic grammars define a set of well-formed or grammatical linguistic structures, just as all grammars do. But in addition probabilistic grammars also define probability distributions over these structures, which a statistical inference procedure can exploit. This paper describes two simple statistical inference procedures for probabilistic grammars and discusses some of the challenge
The use of language is one of the defining features of human cognition. Focusing here on two key fea...
Building models of language is a central task in natural language processing. Traditionally, languag...
We examine the utility of a curriculum (a means of presenting training samples in a meaningful order...
This paper shows how to define probability distributions over linguistically realistic syntactic str...
The purpose of this paper is to define the framework within which empirical investigations of probab...
Recent computational research on natural language corpora has revealed that relatively simple statis...
There is much debate over the degree to which language learning is governed by innate language-speci...
There is much debate over the degree to which language learning is governed by innate language-speci...
It is quite natural to assign probabilities (or frequencies) to the sentences of a language to try t...
A mathematical formulation of probabilistic grammars, as well as the random languages generated by p...
[18]. Section 1 provides the proofs of the theorems in Section 3 of the paper. Section 2 gives more ...
The notion of language as probabilistic is well known within Systemic Functional Linguistics. Aspect...
Probabilistic grammars offer great flexibility in modeling discrete sequential data like natural lan...
Acquiring language is notoriously complex, yet for the majority of children this feat is accomplishe...
Probabilistic methods are providing new explanatory approaches to fundamental cognitive science ques...
The use of language is one of the defining features of human cognition. Focusing here on two key fea...
Building models of language is a central task in natural language processing. Traditionally, languag...
We examine the utility of a curriculum (a means of presenting training samples in a meaningful order...
This paper shows how to define probability distributions over linguistically realistic syntactic str...
The purpose of this paper is to define the framework within which empirical investigations of probab...
Recent computational research on natural language corpora has revealed that relatively simple statis...
There is much debate over the degree to which language learning is governed by innate language-speci...
There is much debate over the degree to which language learning is governed by innate language-speci...
It is quite natural to assign probabilities (or frequencies) to the sentences of a language to try t...
A mathematical formulation of probabilistic grammars, as well as the random languages generated by p...
[18]. Section 1 provides the proofs of the theorems in Section 3 of the paper. Section 2 gives more ...
The notion of language as probabilistic is well known within Systemic Functional Linguistics. Aspect...
Probabilistic grammars offer great flexibility in modeling discrete sequential data like natural lan...
Acquiring language is notoriously complex, yet for the majority of children this feat is accomplishe...
Probabilistic methods are providing new explanatory approaches to fundamental cognitive science ques...
The use of language is one of the defining features of human cognition. Focusing here on two key fea...
Building models of language is a central task in natural language processing. Traditionally, languag...
We examine the utility of a curriculum (a means of presenting training samples in a meaningful order...