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        <title>Numbers Matter! Bringing Quantity-awareness to Retrieval Systems</title>
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            <namePart type="given">Mohit</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2024.findings-emnlp.707</identifier>
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%0 Conference Proceedings
%T Numbers Matter! Bringing Quantity-awareness to Retrieval Systems
%A Almasian, Satya
%A Bruseva, Milena
%A Gertz, Michael
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F almasian-etal-2024-numbers
%X 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.
%R 10.18653/v1/2024.findings-emnlp.707
%U https://aclanthology.org/2024.findings-emnlp.707/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.707
%P 12120-12136

Markdown (Informal)

[Numbers Matter! Bringing Quantity-awareness to Retrieval Systems](https://aclanthology.org/2024.findings-emnlp.707/) (Almasian et al., Findings 2024)

• Numbers Matter! Bringing Quantity-awareness to Retrieval Systems (Almasian et al., Findings 2024)

ACL

• Satya Almasian, Milena Bruseva, and Michael Gertz. 2024. Numbers Matter! Bringing Quantity-awareness to Retrieval Systems. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 12120–12136, Miami, Florida, USA. Association for Computational Linguistics.