Another approach for generating query and image representations is treating images as a black box. Given the statistics of our dataset (3B query, image pairs with 220M unique queries and 900M unique images), we know that different queries co-occur with the same images. Intuitively, if a query $q_1$ co-occurs with many of the same images as query $q_2$ , then $q_1$ and $q_2$ are likely to be semantically similar, regardless of the visual content of the shared images. Thus, we can use a method that uses only co-occurrence statistics to better understand how well we can capture relationships between queries. 
 Question: Could you learn such embedding simply from the image annotations and without using visual information?
Yes