Add to ORKG[18]. Section 1 provides the proofs of the theorems in Section 3 of the paper. Section 2 gives more details of the experimental settings and results. Section 3 discusses the related work
There is much debate over the degree to which language learning is governed by innate language-speci...
We present an efficient learning algorithm for probabilistic context-free grammars based on the vari...
By employing intuitive heuristics, people are prone to make erroneous intuitive judgments on uncerta...
We examine the utility of a curriculum (a means of presenting training samples in a meaningful order...
Probabilistic grammars define a set of well-formed or grammatical linguistic structures, just as all...
The purpose of this paper is to define the framework within which empirical investigations of probab...
This article provides a critical assessment of the Gradual Learning Algorithm (GLA) for probabilisti...
The problem of identifying a probabilistic context free grammar has twoaspects: the first is determi...
Probabilistic grammars offer great flexibility in modeling discrete sequential data like natural lan...
Recent computational research on natural language corpora has revealed that relatively simple statis...
This paper shows how to define probability distributions over linguistically realistic syntactic str...
Abstract. This paper presents PCFG-BCL, an unsupervised algorithm that learns a probabilistic contex...
Recently, different theoretical learning results have been found for a variety of context-free gramm...
One argument for parametric models of language has been learnability in the context of first languag...
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...
We present an efficient learning algorithm for probabilistic context-free grammars based on the vari...
By employing intuitive heuristics, people are prone to make erroneous intuitive judgments on uncerta...
We examine the utility of a curriculum (a means of presenting training samples in a meaningful order...
Probabilistic grammars define a set of well-formed or grammatical linguistic structures, just as all...
The purpose of this paper is to define the framework within which empirical investigations of probab...
This article provides a critical assessment of the Gradual Learning Algorithm (GLA) for probabilisti...
The problem of identifying a probabilistic context free grammar has twoaspects: the first is determi...
Probabilistic grammars offer great flexibility in modeling discrete sequential data like natural lan...
Recent computational research on natural language corpora has revealed that relatively simple statis...
This paper shows how to define probability distributions over linguistically realistic syntactic str...
Abstract. This paper presents PCFG-BCL, an unsupervised algorithm that learns a probabilistic contex...
Recently, different theoretical learning results have been found for a variety of context-free gramm...
One argument for parametric models of language has been learnability in the context of first languag...
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...
We present an efficient learning algorithm for probabilistic context-free grammars based on the vari...
By employing intuitive heuristics, people are prone to make erroneous intuitive judgments on uncerta...