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Typology emerges from simplicity in representations and learning

Lambert Dakotah, Rawski Jonathan, Heinz Jeffrey

Journal of Language Modelling

2021

Vol. 9, No. 1

151--194

We derive well-understood and well-studied subregular classes of formal languages purely from the computational perspective of algorithmic learning problems. We parameterise the learning problem along dimensions of representation and inference strategy. Of special interest are those classes of languages whose learning algorithms are necessarily not prohibitively expensive in space and time, since learners are often exposed to adverse conditions and sparse data. Learned natural language patterns are expected to be most like the patterns in these classes, an expectation supported by previous typological and linguistic research in phonology. A second result is that the learning algorithms presented here are completely agnostic to choice of linguistic representation. In the case of the subregular classes, the results fall out from traditional model-theoretic treatments of words and strings. The same learning algorithms, however, can be applied to model-theoretic treatments of other linguistic representations such as syntactic trees or autosegmental graphs, which opens a useful direction for future research.

Extracting Subregular constraints from Regular stringsets

Rogers James, Lambert Dakotah

Journal of Language Modelling

2019

Vol. 7, No. 2

143--176

We introduce algorithms that, given a finite-state automaton (FSA), compute a minimal set of forbidden local factors that define a Strictly Local (SL) tight approximation of the stringset recognised by the FSA and the set of forbidden piecewise factors that define a Strictly Piecewise (SP) tight approximation of that stringset, as well as a set of co-SL factors that, together with the SL and SP factors, provide a set of purely conjunctive literal constraints defining a minimal superset of the stringset recognised by the automaton. Using these, we have built computational tools that have allowed us to reproduce, by nearly purely computational means, the work of Rogers and his co-workers (Rogers et al. 2012) in which, using a mix of computational and analytical techniques, they completely characterised, with respect to the Local and Piecewise Subregular hierarchies, the constraints on the distribution of stress in human languages that are documented in the StressTyp2 database. Our focus, in this paper, is on the algorithms and the method of their application. The phonology of stress patterns is a particularly good domain of application since, as we show here, they generally fall at the very lowest levels of complexity. We discuss these phonological results here, but do not consider their consequences in depth.