When you compute similarity between two words, do you just trust the number, or do you inspect which senses were matched? One reason I built WordNet Explorer was to make similarity explainable at the synset level.
Curious how others approach this when building NLP systems.
WordNet Explorer is a visual NLP toolkit for exploring word senses (synsets), comparing semantic similarity, analyzing text, and visualizing lexical relationships. Inspect definitions, lemmas, hypernyms, and similarity scores in a clean web UI, or integrate via API for your own language-powered applications.
