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        <title>Measurement Extraction with Natural Language Processing: A Review</title>
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    <name type="personal">
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        <name type="personal">
            <namePart type="given">Yue</namePart>
            <namePart type="family">Zhang</namePart>
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    <abstract>Quantitative data is important in many domains. Information extraction methods draw structured data from documents. However, the extraction of quantities and their contexts has received little attention in the history of information extraction. In this review, an overview of prior work on measurement extraction is presented. We describe different approaches to measurement extraction and outline the challenges posed by this task. The review concludes with an outline of potential future research. Research strains in measurement extraction tend to be isolated and lack a common terminology. Improvements in numerical reasoning, more extensive datasets, and the consideration of wider contexts may lead to significant improvements in measurement extraction.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.161</identifier>
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%0 Conference Proceedings
%T Measurement Extraction with Natural Language Processing: A Review
%A Göpfert, Jan
%A Kuckertz, Patrick
%A Weinand, Jann
%A Kotzur, Leander
%A Stolten, Detlef
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F gopfert-etal-2022-measurement
%X Quantitative data is important in many domains. Information extraction methods draw structured data from documents. However, the extraction of quantities and their contexts has received little attention in the history of information extraction. In this review, an overview of prior work on measurement extraction is presented. We describe different approaches to measurement extraction and outline the challenges posed by this task. The review concludes with an outline of potential future research. Research strains in measurement extraction tend to be isolated and lack a common terminology. Improvements in numerical reasoning, more extensive datasets, and the consideration of wider contexts may lead to significant improvements in measurement extraction.
%R 10.18653/v1/2022.findings-emnlp.161
%U https://aclanthology.org/2022.findings-emnlp.161
%U https://doi.org/10.18653/v1/2022.findings-emnlp.161
%P 2191-2215

Markdown (Informal)

[Measurement Extraction with Natural Language Processing: A Review](https://aclanthology.org/2022.findings-emnlp.161) (Göpfert et al., Findings 2022)

• Measurement Extraction with Natural Language Processing: A Review (Göpfert et al., Findings 2022)

ACL

• Jan Göpfert, Patrick Kuckertz, Jann Weinand, Leander Kotzur, and Detlef Stolten. 2022. Measurement Extraction with Natural Language Processing: A Review. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 2191–2215, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.