PURPOSE Even without explicit instruction, learners are able to extract information about the form of a language simply by attending to input that reflects the underlying grammar. In this study, the authors explored the role of variability in this learning by asking whether varying the number of unique exemplars heard by the learner affects learning of an artificial syntactic form. METHOD Lea...

Language learners must acquire the grammar (rules, constraints, principles) of their language as well as representations at various levels. I will argue that representations are part of the grammar and must be acquired together with other aspects of grammar; thus, grammar acquisition may not presuppose knowledge of representations. Further, I will argue that the goal of a learning model should ...

Natural Language is highly ambiguous, on every level. This article describes a fast broadcoverage state-of-the-art parser that uses a carefully hand-written grammar and probabilitybased machine learning approaches on the syntactic level. It is shown in detail which statistical learning models based on Maximum-Likelihood Estimation (MLE) can support a highly developed linguistic grammar in the d...