LEARNING-THEORETIC FOUNDATIONS OF LINGUISTIC UNIVERSALS

Linguistic diversity and universals

Cognitive Biases, Linguistic Universals, and Constraint-Based Grammar Learning

According to classical arguments, language learning is both facilitated and constrained by cognitive biases. These biases are reflected in linguistic typology-the distribution of linguistic patterns across the world's languages-and can be probed with artificial grammar experiments on child and adult learners. Beginning with a widely successful approach to typology (Optimality Theory), and adapt...

On the question of linguistic universals

This article offers a general discussion of the concept of universals in linguistics (and in general), spelling out different ways of understanding claims to universality and connecting such claims to other (often familiar) related distinctions, terminology and approaches such as competence and performance or I-language and E-language, evolutionary explanations, deep and surface universals, rat...

Quantitative Standards for Absolute Linguistic Universals

Absolute linguistic universals are often justified by cross-linguistic analysis: If all observed languages exhibit a property, the property is taken to be a likely universal, perhaps specified in the cognitive or linguistic systems of language learners and users. In many cases, these patterns are then taken to motivate linguistic theory. Here, we show that cross-linguistic analysis will very ra...

Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems

Max-cut, clustering, and many other partitioning problems that are of significant importance to machine learning and other scientific fields are NP-hard, a reality that has motivated researchers to develop a wealth of approximation algorithms and heuristics. Although the best algorithm to use typically depends on the specific application domain, a worst-case analysis is often used to compare al...