After trying a few searches of my own it's easy to see how this could come in handy, especially when it comes to searching for queries with multiple, very different meanings (like "Sierra Nevada", the beer or the mountain range).
However, all people have multiple identities, which take on different meanings and importance in different contexts.
At its heart it uses its training data (a huge collection of text) to determine whether a word has multiple meanings and how those different meanings are signaled in language.
Finally, a single word may have multiple count-classifiers that convey different meanings altogether in fact, the choice of a classifier can even influence the meaning of a noun.
Sometimes, similar terms have different meanings, and often, there are multiple terms with different meanings, and multiple terms for the same phenomenon or process.
"Power has different meanings.
Of course speed has different meanings.
"Surprise has two very different meanings.
And these words have different meanings.
They all find different meanings.
