Probabilistically cued co-occurrence relationships between word categories are common in
natural languages but difficult to acquire. For example, in English, determiner-noun and …

Natural languages contain many layers of sequential structure, from the distribution of
phonemes within words to the distribution of phrases within utterances. However, most …

Computational models have shown that purely statistical knowledge about words' linguistic
contexts is sufficient to learn many properties of words, including syntactic and semantic …

An important aspect of language acquisition involves learning the syntactic nonadjacent
dependencies that hold between words in sentences, such as subject/verb agreement or …

Revised 04 February 2016–Accepted 10 February 2016) abstract This paper presents an
algorithm for learning the construction grammar of a language from a large corpus. This …

This study examines whether human learners can acquire statistics over abstract categories
and their relationships to each other. Adult learners were exposed to miniature artificial …

A crucial component of language acquisition involves organizing words into grammatical
categories and discovering relations between them. Many studies have argued that …

Recent computational research on natural language corpora has revealed that relatively
simple statistical learning mechanisms can make an important contribution to certain …

A main objective of this book has been to illustrate how probabilistic inference over rich
structured representations provides a powerful machinery for modelling human intelligence …

Fluency in a language requires understanding abstract relationships between types or
classes of words—the syntax of language. The learning problem has seemed so …