1
Your word
measured
Definitions come from WordNet, a hand-curated dictionary.
a person who comes to a country where they were not born in order to settle there
immigrant is French for immigrants, 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.
Definitions come from WordNet, a hand-curated dictionary.
This panel splits immigrant itself, not immigrants — 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 immigrants, 1 of 8 also appear in the dictionary.
Shade shows how close the model puts each word to immigrants, 1 of 8 also appear in the dictionary.
Shade shows how close the model puts each word to immigrants, 1 of 8 also appear in the dictionary.
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 immigrants. 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 immigrants. 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 immigrants. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.