Prevalence and recoverability of syntactic parameters in sparse distributed memories
This work addresses a specific problem in linguistics for researchers studying syntactic dependencies, but it is incremental as it applies an existing method to new data without broad impact.
The authors tackled the problem of understanding dependency relations between syntactic parameters in world languages by using Sparse Distributed Memory (Kanerva Networks) to store and recover corrupted parameter data, finding that parameters vary in recoverability with some linked to prevalence and others showing additional dependencies.
We propose a new method, based on Sparse Distributed Memory (Kanerva Networks), for studying dependency relations between different syntactic parameters in the Principles and Parameters model of Syntax. We store data of syntactic parameters of world languages in a Kanerva Network and we check the recoverability of corrupted parameter data from the network. We find that different syntactic parameters have different degrees of recoverability. We identify two different effects: an overall underlying relation between the prevalence of parameters across languages and their degree of recoverability, and a finer effect that makes some parameters more easily recoverable beyond what their prevalence would indicate. We interpret a higher recoverability for a syntactic parameter as an indication of the existence of a dependency relation, through which the given parameter can be determined using the remaining uncorrupted data.