Enhancing entity resolution with multichannel BERT: a comprehensive approach

Mingming Geng

doi:10.1117/12.3031934

8 June 2024 Enhancing entity resolution with multichannel BERT: a comprehensive approach

Mingming Geng

Proceedings Volume 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024); 1317126 (2024) https://doi.org/10.1117/12.3031934
Event: 3rd International Conference on Algorithms, Microchips and Network Applications (AMNA 2024), 2024, Jinan, China

Abstract

One of the primary challenges in integrating large-scale data sources is entity resolution, which involves linking records that refer to the same entity. In recent years, deep learning has emerged as a proposed solution for addressing entity resolution. however, insufficient feature extraction and inadequate feature integration during the entity resolution process have resulted in sub-optimal results. In this paper, Multi-Channel BERT for Entity Resolution (MCBER) is proposed, a method that involves first translating the target data into different languages and utilizing data augmentation to expand the labeled data. Then, these data are fed into a multi-channel BERT model for feature extraction, followed by deeper feature extraction using LSTM. Finally, abstract features are induced from hidden layers. Our method is compared with state-of-the-art entity resolution methods on publicly available datasets, and the experimental results demonstrate that higher F1 scores are achieved by our approach, and good stability is exhibited.

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

Citation Download Citation

Mingming Geng "Enhancing entity resolution with multichannel BERT: a comprehensive approach", Proc. SPIE 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024), 1317126 (8 June 2024); https://doi.org/10.1117/12.3031934

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KEYWORDS

Feature extraction

Data modeling

Education and training

Deep learning

Machine learning

Active learning

Ablation

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