Detecting semantic difference: A new model based on knowledge and collocational association

TL;DR: This study proposes a novel automatic approach to detect whether an attribute word represents the difference between two given words, exploiting a combination of knowledge-based and co-occurrence features (collocations) to capture the semantic difference betweenTwo words in relation to an attribute.

Abstract: Semantic discrimination among concepts is a daily exercise for humans when using natural languages. For example, given the words, airplane and car , the word flying can easily be thought and used as an attribute to differentiate them. In this study, we propose a novel automatic approach to detect whether an attribute word represents the difference between two given words. We exploit a combination of knowledge-based and co-occurrence features (collocations) to capture the semantic difference between two words in relation to an attribute. The features are scores that are defined for each pair of words and an attribute, based on association measures, n-gram counts, word similarity, and Concept-Net relations. Based on these features we designed a system that run several experiments on a SemEval-2018 dataset. The experimental results indicate that the proposed model performs better, or at least comparable with, other systems evaluated on the same data for this task.