Numbers Matter! Bringing Quantity-awareness to Retrieval Systems
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929420302811136 |
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| author | Almasian, Satya Bruseva, Milena Gertz, Michael |
| author_facet | Almasian, Satya Bruseva, Milena Gertz, Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10283 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Numbers Matter! Bringing Quantity-awareness to Retrieval Systems Almasian, Satya Bruseva, Milena Gertz, Michael Information Retrieval Machine Learning H.3.3; I.2.7 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. |
| title | Numbers Matter! Bringing Quantity-awareness to Retrieval Systems |
| topic | Information Retrieval Machine Learning H.3.3; I.2.7 |
| url | https://arxiv.org/abs/2407.10283 |