  Millions of people turn to Google Search each day for information on things
as diverse as new cars or flu symptoms. The terms that they enter contain
valuable information on their daily intent and activities, but the information
in these search terms has been difficult to fully leverage. User-defined
categorical filters have been the most common way to shrink the dimensionality
of search data to a tractable size for analysis and modeling. In this paper we
present a new approach to reducing the dimensionality of search data while
retaining much of the information in the individual terms without user-defined
rules. Our contributions are two-fold: 1) we introduce SLaM Compression, a way
to quantify search terms using pre-trained language models and create a
representation of search data that has low dimensionality, is memory efficient,
and effectively acts as a summary of search, and 2) we present CoSMo, a
Constrained Search Model for estimating real world events using only search
data. We demonstrate the efficacy of our contributions by estimating with high
accuracy U.S. automobile sales and U.S. flu rates using only Google Search
data.
