Semantic structure in the mental lexicon is often assumed to follow a taxonomic structure grouping similar items. This study uses a network clustering analysis of a massive word association dataset that does not primarily focus on concrete noun categories, but includes the majority of the words used in daily life. At this scale, we found widespread overlap between thematically organized clusters, arguing against a discrete categoric view of the lexicon. An empirical analysis focusing on taxonomic categories confirmed the widespread thematic structure even for concrete noun categories in the animal domain. Overall, this suggests that applying network clustering to word association data provides valuable insight into how large-scale semantic information is represented. This analysis leads to a different, more thematic topology than the one inferred from idealized small-scale approaches that sample only specific parts of the lexicon.