William Gregory Sakas

Disambiguating Syntactic Triggers.

We present data from an artificial language domain that suggest new contributions to the theory of syntactic triggers. Whether a learning algorithm is capable of matching the achievements of child learners depends in part on how much parametric ambiguity there is in the input. For practical reasons this cannot be established for the domain of all natural languages. Our tactic is to estimate the incidence of unambiguous triggers by examining a constructed domain of languages whose syntactic parameters and structural properties are precisely specified. We succeeded in identifying unambiguous triggers for all non-default parameter values in all languages in this domain. In order to do so, we had to invoke between-parameter relations which disambiguate triggers that otherwise would have been parametrically ambiguous. The discovery of unambiguous triggers in this artificial domain does not prove a sufficiency in natural languages, but it may revive investigation of the psychological plausibility of deterministic models of human language acquisition.

A Word-Order Database for Testing Computational Models of Language Acquisition

An investment of effort over the last two years has begun to produce a wealth of data concerning computational psycholinguistic models of syntax acquisition. The data is generated by running simulations on a recently completed database of word order patterns from over 3,000 abstract languages. This article presents the design of the database which contains sentence patterns, grammars and derivations that can be used to test acquisition models from widely divergent paradigms. The domain is generated from grammars that are linguistically motivated by current syntactic theory and the sentence patterns have been validated as psychologically/developmentally plausible by checking their frequency of occurrence in corpora of child-directed speech. A small case-study simulation is also presented.