Prevalence and recoverability of syntactic parameters in sparse distributed memories

TL;DR

This paper introduces a novel method using Sparse Distributed Memory to analyze syntactic parameter dependencies across languages, revealing varying degrees of recoverability that suggest underlying syntactic relations.

Contribution

It presents a new approach to studying syntactic dependencies by applying Kanerva Networks to language data, highlighting differential recoverability of parameters.

Findings

Different syntactic parameters show varying recoverability levels.

Higher recoverability indicates potential dependency relations among parameters.

Some parameters are more easily recoverable than their prevalence suggests.

Abstract

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.