Many Speakers, Many Worlds

Aurélie Herbelot, Eva Maria Vecchi

Abstract


In this paper, we present an annotation layer for the McRae feature norms, providing quantificational labels (no, few, some, most, all) for each of the 6120 concept-feature pairs in the original dataset. Our analysis of the produced data shows that while annotators agree to a large extent on their choice of label (reaching 0.59 average weighted kappa), significant differences can also be found between coders. This leads us to propose the 'many speakers, many worlds' hypothesis: a) speakers do not share a single view of the world, and variations can be straightforwardly attested through an explicit quantification task; b) such variations are erased in natural language thanks to the overwhelming use of generic or vague quantification, which can be seen as supporting communication efficiency.

Keywords


quantification; annotation; semantic relations;

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References


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