Identifying and Explaining Discriminative Attributes

Abstract

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-ofthe-art systems in the task (F1-score = 0.69), while delivering full model transparency and explainability.

Scientific Explanation and Natural Language: A Unified Epistemological-Linguistic Perspective for Explainable AI

“…We attribute this result to pragmatic aspects and inference requirements associated to the unification process. Definitions, for instance, might serve both as a way to introduce missing context and background knowledge in natural language discourse and, in parallel, as a mechanism for abstraction, relating specific terms to high-level conceptual categories (Silva et al, 2018;Silva et al, 2019;Stepanjans and Freitas, 2019).…”