This work is a companion reproducibility paper of the experiments and results reported in Lastra-Diaz et al. (2019a), which is based on the evaluation of a companion reproducibility dataset with the HESML V1R4 library and the long-term reproducibility tool called Reprozip. Human similarity and relatedness judgements between concepts underlie most of cognitive capabilities, such as categorization, memory, decision-making and reasoning. For this reason, the research on methods for the estimation of the degree of similarity and relatedness between words and concepts has received a lot of attention in the fields of artificial intelligence and cognitive sciences. However, despite the huge research effort done, there is a lack of a self-contained, reproducible and extensible collection of benchmarks which being amenable to become a de facto standard for large scale experimentation in this line of research. In order to bridge this reproducibility gap, this work introduces a set of reproducible experiments on word similarity and relatedness by providing a detailed reproducibility protocol together with a set of software tools and a self-contained reproducibility dataset, which allow that all experiments and results in our aforementioned work to be reproduced exactly. Our aforementioned primary work introduces the largest, most detailed and reproducible experimental survey on word similarity and relatedness reported in the literature, which is based on the implementation of all evaluated methods into the same software platform. Our reproducible experiments evaluate most of methods in the families of ontology-based semantic similarity measures and word embedding models. We also detail how to extend our experiments to evaluate other unconsidered experimental setups. Finally, we provide a corrigendum for a mismatch in the MC28 similarity scores used in our original experiments.

这项工作是该实验的伴随重现性论文，其结果报告于Lastra-Diaz等人。（2019a），该评估基于使用HESML V1R4库和称为Reprozip的长期再现性工具对同伴再现性数据集的评估。概念之间的人类相似性和相关性判断是大多数认知能力的基础，例如分类，记忆，决策和推理。因此，用于估计单词和概念之间的相似度和相关度的方法的研究在人工智能和认知科学领域受到了广泛的关注。但是，尽管进行了大量的研究工作，但缺少一个自成体系的，可重复和可扩展的基准集合，这些基准将成为该研究领域中大规模实验的事实上的标准。为了弥合这种可重复性差距，这项工作通过提供详细的可重复性协议以及一套软件工具和一个自包含的可重复性数据集，引入了一组针对单词相似性和相关性的可重复性实验，从而可以进行所有实验和得出结果。我们前面提到的作品将被精确复制。我们前面提到的主要工作介绍了文献中有关词相似性和相关性的最大，最详细和可再现的实验调查，该调查基于将所有评估方法实施到同一软件平台中的基础。我们的可重现实验评估了基于本体的语义相似性度量和词嵌入模型家族中的大多数方法。我们还将详细介绍如何扩展我们的实验以评估其他未考虑的实验设置。最后，我们提供了原始实验中使用的MC28相似性评分不匹配的勘误。