This paper [4] – referred to below as ‘LXL’ – is an excellent example of cross-disciplinary work which brings together three very different disciplines, each with its different methods: quantitative computational linguistics (exploring big data), psycholinguistics (using experiments with human subjects) and theoretical linguistics (building models based on language descriptions). The measured unit is the dependency between two words, as defined by theoretical linguistics, and the question is how the length of this dependency affects the choices made by writers, as revealed in big data from a wide range of languages. LXL’s main claim is that, as speakers and writers, we tend to minimize dependency distance; the comparisons with random structures provide convincing evidence for this claim. LXL also offer an explanation for this tendency: that it is the result of the way in which grammars have evolved. As many theoretical linguists claim, this evolution is guided by a multiplicity of functional pressures, and LXL establish that minimization of dependency distance is one of these pressures. They also relate this tendency to the relative processing difficulties encountered by readers and recorded in psycholinguistic experiments, and caused, ultimately, by limitations on working memory.
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