Papers › Identifying and Explaining Discriminative Attributes

Identifying and Explaining Discriminative Attributes

5 Sep 2019IJCNLP 2019 11arXiv:1909.05363archive 2025-07-28

Armins Stepanjans, André Freitas

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.

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Tasks

AttributeKnowledge GraphsNatural Language InferenceRelation Extraction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Relation Extraction SemEval 2018 Task 10 Composes explicit vector spaces from WordNet Definitions, ConceptNet and Visual Genome F1-Score 0.69 #5 of 6 Archive leaderboard report

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