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 exhibit
more disagreement. Yet, collected rating norms are heavily exploited across
disciplines. Our study focuses on concreteness ratings and (i) implements
correlations and supervised classification to identify salient multi-modal
characteristics of mid-scale words, and (ii) applies a hard clustering to
identify patterns of systematic disagreement across raters. Our results suggest
to either fine-tune or filter mid-scale target words before utilising them.

我们的研究关注中等规模的单词的具体性评分，并通过相关性和监督分类来识别显著的多模式特征，应用硬聚类方法来确定评分者之间系统性分歧的模式。结果表明，在利用中等规模的目标词之前，需要对其进行微调或过滤。

中等规模评级的争议性质研究：抽象性 - 具体性连续性案例研究

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

We present a versatile adaptation of existing dimensionality reduction (DR)
objectives, enabling the simultaneous reduction of both sample and feature
sizes. Correspondances between input and embedding samples are computed through
a semi-relaxed Gromov-Wasserstein optimal transport (OT) problem. When the
embedding sample size matches that of the input, our model recovers classical
popular DR models. When the embedding's dimensionality is unconstrained, we
show that the OT plan delivers a competitive hard clustering. We emphasize the
importance of intermediate stages that blend DR and clustering for summarizing
real data and apply our method to visualize datasets of images.

我们提出了一种适用于现有降维目标的通用适应方法，同时降低样本和特征大小。通过半松弛的 Gromov-Wasserstein 最优输运问题计算输入和嵌入样本之间的对应关系。当嵌入样本大小与输入大小匹配时，我们的模型恢复了经典的受欢迎的降维模型。当嵌入的维度不受限制时，我们展示了最优输运方案提供了一种有竞争力的硬聚类。我们强调了中间阶段将降维和聚类相结合以概括真实数据的重要性，并将我们的方法应用于可视化图像数据集。