Linguistic typology generally divides synthetic languages into groups based on their morphological fusion. However, this measure has long been thought to be best considered a matter of degree. We present an information-theoretic measure, called informational fusion, to quantify the degree of fusion of a given set of morphological features in a surface form, which naturally provides such a graded scale. Informational fusion is able to encapsulate not only concatenative, but also nonconcatenative morphological systems (e.g. Arabic), abstracting away from any notions of morpheme segmentation. We then show, on a sample of twenty-one languages, that our measure recapitulates the usual linguistic classifications for concatenative systems, and provides new measures for nonconcatenative ones. We also evaluate the long-standing hypotheses that more frequent forms are more fusional, and that paradigm size anticorrelates with degree of fusion. We do not find evidence for the idea that languages have characteristic levels of fusion; rather, the degree of fusion varies across part-of-speech within languages.

This paper compares two influential theories of processing difficulty: Gibson (2000)’s Dependency Locality Theory (DLT) and Hale (2001)’s Surprisal Theory. While prior work has aimed to compare DLT and Surprisal Theory (see Demberg and Keller, 2008), they have not yet been compared using more modern and powerful methods for estimating surprisal and DLT integration cost. I compare estimated surprisal values from two models, an RNN and a Transformer neural network, as well as DLT integration cost from a hand-parsed treebank, to reading times from the Dundee Corpus. Our results for integration cost corroborate those of Demberg and Keller (2008), finding that it is a negative predictor of reading times overall and a strong positive predictor for nouns, but contrast with their observations for surprisal, finding strong evidence for lexicalized surprisal as a predictor of reading times. Ultimately, I conclude that a broad-coverage model must integrate both theories in order to most accurately predict processing difficulty.