Identifying Minimalist Languages from Dependency Structures

The human acquisition of human languages is is based on the analysis of signal and context. To study how this might work, a simplified robotic setting is described in which the problem is divided into two basic steps: an analysis of the linguistic events in context that yields dependency structures, and the identification of grammars that generate those structures. A learnability result that generalizes (Kanazawa 1994) has been obtained, showing that non-CF, and even non-TAG languages can be identified in this setting, andmore realistic assessments of the learning problem are under study. Human languages are complex, and it is a challenge to find strategies which will let us understand their components. (In the same way, the physical properties of the table that my papers are on is complex, and so it is no surprise that physicists and chemists do not directly study tables, but simpler components of tables, first.) The strategy I will describe here is to study some things that are similar to human languages in some crucial respects, but otherwise very much simpler. Imagine a simplified language learner who cannot speak or hear but only read and write. Imagine this learner can see and reason (but perhaps only in some quite primitive ways), and that this learner encounters expressions – character sequences – like this in certain perceptual and cognitive contexts. language learned in context John ’s eat -ing cookie -s covariation with context: choice of elements: John vs Bill, eat vs buy, cookie vs pie configuration: cookies, John’s eating s’John eating cookies?