Patch-Based Identification of Lexical Semantic Relations

The identification of lexical semantic relations is of the utmost importance to enhance reasoning capacities of Natural Language Processing and Information Retrieval systems. Within this context, successful results have been achieved based on the distributional hypothesis and/or the paradigmatic assumption. However, both strategies solely rely on the input words to predict the lexical semantic relation. In this paper, we make the hypothesis that the decision process should not only rely on the input words but also on their K closest neighbors in some semantic space. For that purpose, we present different binary and multi-task classification strategies that include two distinct attention mechanisms based on PageRank. Evaluation results over four gold-standard datasets show that average improvements of 10.6% for binary and 8% for multi-task classification can be achieved over baseline approaches in terms of F[Formula: see text]. The code and the datasets are available upon demand.