Identifying what is at the center of the meaning of a word and what discriminates it from other words is a fundamental natural language inference task. This paper describes an explicit word vector representation model (WVM) to support the identification of discriminative attributes. A core contribution of the paper is a quantitative and qualitative comparative analysis of different types of data sources and Knowledge Bases in the construction of explainable and explicit WVMs: (i) knowledge graphs built from dictionary definitions, (ii) entity-attribute-relationships graphs derived from images and (iii) commonsense knowledge graphs. Using a detailed quantitative and qualitative analysis, we demonstrate that these data sources have complementary semantic aspects, supporting the creation of explicit semantic vector spaces. The explicit vector spaces are evaluated using the task of discriminative attribute identification, showing comparable performance to the state-of-the-art systems in the task (F1-score = 0.69), while delivering full model transparency and explainability.

本文提出了一种明确的单词向量表示模型（WVM），并使用三种不同类型的数据库：字典定义，图像和常识知识图谱，旨在支持较好的歧义词汇特征识别。这些数据源具有互补语义方面，展示了明确向量空间的创建过程，最终比较了这些向量空间对于歧义词汇识别的性能表现。结果表明，明确向量空间具有很高的可解释性和透明性，并且在该任务中表现出相当竞争的性能。

辨识和解释有差别的属性