Abstract
As a set of essential information related to the quantity of objects, measurable quantitative information widely exists in various texts. This paper proposes a new span-based joint model with lexicon enhanced BERT for measurable quantitative information extraction, which incorporates both measurable quantitative information recognition and association within one model. A standard dataset containing 3,106 Chinese text sentences and a clinical dataset containing 1,359 Chinese electronic medical records are used for evaluation. Experiment results show that our joint model achieves an F1-score of 97.44% on the clinical dataset and outperforms baseline models by an improvement of 3.2% for recognition and 5.1% for association in F1-score on the standard dataset.
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Acknowledgements
The work is supported by grants from National Natural Science Foundation of China (No. 61871141), Natural Science Foundation of Guangdong Province (2021A1515011339), Presidential Foundation of China National Institute of Standardization (522022Y-9406), and Collaborative Innovation Team of Guangzhou University of Traditional Chinese Medicine (No. 2021XK08).
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Mo, D., Huang, B., Wang, H., Cao, X., Weng, H., Hao, T. (2022). A Span-Based Joint Model for Measurable Quantitative Information Extraction. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1638. Springer, Singapore. https://doi.org/10.1007/978-981-19-6135-9_26
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DOI: https://doi.org/10.1007/978-981-19-6135-9_26
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