CDM-IE: comprehensive dependence mining for document-level information extraction

Chao Wang; Qian Chang; Longgang Zhao; Qianlan Zhou

doi:10.1117/12.3051572

18 November 2024 CDM-IE: comprehensive dependence mining for document-level information extraction

Chao Wang, Qian Chang, Longgang Zhao, Qianlan Zhou

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 1340314 (2024) https://doi.org/10.1117/12.3051572
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

Information extraction is a fundamental aspect of natural language understanding tasks. Previous works generally utilized a transformer-based architecture as a text encoder. However, gradient vanishing and attention dispersion tend to be inevitable when tacking document-level paragraph texts, which have negative effects on capturing global token relationships. To address these limitations, we propose CDM-IE, an information extraction approach specifically designed for lengthy text input. The CDM-IE is a hybrid CNN-Transformer architecture with dual-branch topology, respectively named a paragraph encoding branch and a dependence mining branch, which excels at learning comprehensive text representation by integrating both global context and local dependence. The two pathways converge at a dependence-guided attention module, which acts as a fusion bridge to feature alignment and synergy. Ablative experiment results on the Medical Entity Extraction (CCKS 2019) and Chinese Machine Reading Comprehension (CMRC 2018) datasets indicate that the proposed CDM-IE showcases improved performance and robustness on information extraction tasks, which provide a valuable solution for text modeling on long sequences.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chao Wang, Qian Chang, Longgang Zhao, and Qianlan Zhou "CDM-IE: comprehensive dependence mining for document-level information extraction", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 1340314 (18 November 2024); https://doi.org/10.1117/12.3051572

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KEYWORDS

Mining

Transformers

Semantics

Ablation

Design

Feature extraction

Modeling

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