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Your word
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“wherever he went in the camp the men were grumbling”
Definitions come from WordNet, a hand-curated dictionary.
temporary living quarters specially built by the army for soldiers
camp is French for camp, and every measurement below was made on that English word. Definitions come from English WordNet; Open Multilingual WordNet supplies the French headword, not a French definition.
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“wherever he went in the camp the men were grumbling”
Definitions come from WordNet, a hand-curated dictionary.
This panel splits camp itself, not camp — a tokenizer does not care what language it is fed. It is the only panel here that does.
This is the tokenizer splitting text, not the model understanding it.
Shade shows how close the model puts each word to camp. 2 of 15 dictionary synonyms are in this build; the rest have no vector to compare yet.
Shade shows how close the model puts each word to camp. 2 of 15 dictionary synonyms are in this build; the rest have no vector to compare yet.
Shade shows how close the model puts each word to camp. 2 of 15 dictionary synonyms are in this build; the rest have no vector to compare yet.
Model neighbours are distributional, not dictionary synonyms. Two words can be close because they appear in similar sentences, which is why an antonym can outscore a synonym here.
Scores are projections onto axes we defined from anchor words, not labels the model assigns.
These sit just outside the closest neighbours in panel 3, and no dictionary lists any of them as related to camp. That gap is the model’s own learned association.
These sit just outside the closest neighbours in panel 3, and no dictionary lists any of them as related to camp. That gap is the model’s own learned association.
These sit just outside the closest neighbours in panel 3, and no dictionary lists any of them as related to camp. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.