Finding Non-Arbitrary Form-Meaning Systematicity Using String-Metric Learning For Kernel Regression

Arbitrariness of the sign the notion that the forms of words are unrelated to their meanings is an underlying assumption of many linguistic theories. Two lines of research have recently challenged this assumption, but they produce differing characterizations of non-arbitrariness in language. Behavioral and corpus studies have confirmed the validity of localized form-meaning patterns manifested in limited subsets of the lexicon. Meanwhile, global (lexicon-wide) statistical analyses instead find diffuse form-meaning systematicity across the lexicon as a whole.We bridge the gap with an approach that can detect both local and global form-meaning systematicity in language. In the kernel regression formulation we introduce, form-meaning relationships can be used to predict words' distributional semantic vectors from their forms. Furthermore, we introduce a novel metric learning algorithm that can learn weighted edit distances that minimize kernel regression error. Our results suggest that the English lexicon exhibits far more global form-meaning systematicity than previously discovered, and that much of this systematicity is focused in localized form-meaning patterns.