  Quantitative information plays a crucial role in understanding and
interpreting the content of documents. Many user queries contain quantities and
cannot be resolved without understanding their semantics, e.g., ``car that
costs less than $10k''. Yet, modern search engines apply the same ranking
mechanisms for both words and quantities, overlooking magnitude and unit
information. In this paper, we introduce two quantity-aware ranking techniques
designed to rank both the quantity and textual content either jointly or
independently. These techniques incorporate quantity information in available
retrieval systems and can address queries with numerical conditions equal,
greater than, and less than. To evaluate the effectiveness of our proposed
models, we introduce two novel quantity-aware benchmark datasets in the domains
of finance and medicine and compare our method against various lexical and
neural models. The code and data are available under
https://github.com/satya77/QuantityAwareRankers.
