BERT
A transformer model Google applied to search that reads context in both directions, so prepositions, negations, and the subtle framing of a long sentence change the interpretation.
Analogy Mental model
An old engine reading a question about getting medicine for someone at a pharmacy would drop the preposition and answer about opening a pharmacy. The newer one understands who the medicine is for.
In practice What you actually do
There is no technical trick here. The only optimisation is writing that answers a specific situation precisely, with the qualifiers intact and the conditions stated, because that is the passage that gets lifted into a featured answer.
Where people get burned Pitfalls
Machine-translated or grammatically broken English scores poorly in this kind of semantic space, even when every keyword is present. Fluency is not decoration; it is how meaning survives the encoding.
This entry was written in English from the Chinese original, not machine-translated. Read the Chinese version →