Semantic Sort: A Supervised Approach to Personalized Semantic Relatedness
Table 1: Experiment 4 (subjective semantic relatedness) - Spearman Correlation.
['Training test size (%)', '0', '0.5', '1', '2', '4', '8']
To examine the ability of SemanticSort to adapt to subjective semantic relatedness ranking, we created two new synthetic sets of semantic relatedness scores to all WordSim353 pairs: A Wikipedia set of scores that was calculated using paragraph-based NSD over Wikipedia; A Gutenberg set that was generated using paragraph-based NSD over the Gutenberg corpus. We consider these two sets as proxies for two different “subjective” semantic relatedness preferences. the first corresponds to learning the Gutenberg preferences using Wikipedia as the BK corpus, and the second, for learning the Wikipedia preferences using Gutenberg as BK. It is evident that in both cases SemanticSort successfully adapted to these subjective preferences achieving excellent test performance in both cases.