Numbers Matter! Bringing Quantity-awareness to Retrieval Systems
This addresses a specific limitation in retrieval systems for users needing precise numerical queries, representing an incremental improvement over existing methods.
The paper tackles the problem of search engines ignoring numerical semantics in queries, introducing quantity-aware ranking techniques that handle conditions like 'less than' or 'greater than', and shows improved performance on new benchmark datasets in finance and medicine.
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.