1
Your word
measured
“set aside a certain sum each week”
Definitions come from WordNet, a hand-curated dictionary.
definite but not specified or identified
ある is Japanese for certain, and every measurement below was made on that English word. Definitions come from English WordNet; Open Multilingual WordNet supplies the Japanese headword, not a Japanese definition.
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“set aside a certain sum each week”
Definitions come from WordNet, a hand-curated dictionary.
This panel splits ある itself, not certain — 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.
uncertain is the dictionary opposite of certain, yet this model puts it closer than 1 of the 2 synonyms it can score. Opposites share the sentences a word lives in, so cosine alone cannot tell them apart - which is why the dictionary seeds this list rather than the geometry.
Shade shows how close the model puts each word to certain, 1 of 8 also appear in the dictionary.
uncertain is the dictionary opposite of certain, yet this model puts it closer than 1 of the 2 synonyms it can score. Opposites share the sentences a word lives in, so cosine alone cannot tell them apart - which is why the dictionary seeds this list rather than the geometry.
Shade shows how close the model puts each word to certain.
uncertain is the dictionary opposite of certain, yet this model puts it closer than 1 of the 2 synonyms it can score. Opposites share the sentences a word lives in, so cosine alone cannot tell them apart - which is why the dictionary seeds this list rather than the geometry.
Shade shows how close the model puts each word to certain, 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 certain. 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 certain. 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 certain. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.