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Humans tend to strongly agree on ratings on a scale for extreme cases (e.g.,a CAT is judged as very concrete), but judgements on mid-scale words exhibitmore disagreement. Yet, collected rating norms are heavily exploited acrossdisciplines. Our study focuses on concreteness ratings and (i) implementscorrelations and supervised classification to identify salient multi-modalcharacteristics of mid-scale words, and (ii) applies a hard clustering toidentify patterns of systematic disagreement across raters. Our results suggestto either fine-tune or filter mid-scale target words before utilising them.

Investigating the Nature of Disagreements on Mid-Scale Ratings: A Case Study on the Abstractness-Concreteness Continuum