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A Data-Free, Universal Prior Distribution for Syntactic Structures

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Across linguistic theories, human language structures are represented by graphs. Much research has focused on the linearisation of such graphs into actual sequences expressing utterances, but less attention has been paid to the shapes that the graphs themselves take: their topology. A current hypothesis from psycholinguistics argues that the structures in human language are primarily shaped by the nature of human language production processes. Utterances are planned in an incremental manner: successively incorporating chunks –either single words, phrases, or even full clauses– into partial syntactic structures. Incremental construction should constrain the plausible probability distributions of syntactic structures in predictable ways. I show that the topologies of actual syntactic graphs exhibit the precise deviation from randomness that incremental construction predicts. This is a previously unknown universal regularity of human languages: Syntactic structures are constrained to a predictable topological distribution –that generated by sublinear preferential attachment– constant for all 124 languages studied, across language families and modalities (spoken, written, and signed). It supports the hypothesis that syntactic structures are mainly shaped by language production. Furthermore, it demonstrates how the observed efficiency of languages might just be epiphenomenal. Crucially, this finding implicitly defines a data-free universal prior distribution for parse structures, with possible applications in language technologies.

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A recording of this talk is available at the following link: https://www.cl.cam.ac.uk/seminars/wednesday/video/

This talk is part of the Wednesday Seminars - Department of Computer Science and Technology series.

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