ELECTRA tops five-model address benchmark on two datasets

ELECTRA achieved the highest reported full-data accuracy in a comparative benchmark of five neural architectures for extracting address components, reaching 99.4% on the OASIS aerospace supplier dataset and 99.6% on the USA Public Schools dataset. The models were evaluated on token-level recognition of city, state, country and postal-code fields, an address-extraction task that can support logistics, customer-data management, record validation and related business-process automation workflows. ELECTRA’s full-data accuracy estimates were reported with 95% confidence intervals of 99.00–99.64% for OASIS and 99.50–99.68% for USA Public Schools.

The study compared WordCNN, WordLSTM, BERT, DistilBERT and ELECTRA using two heterogeneous collections of records. OASIS contained 12,059 instances, whereas USA Public Schools contained 102,370 instances. Experiments were conducted under three data-availability regimes—50%, 80% and 100% of the available data—allowing the researchers to compare model performance as the amount of training information changed. The researchers also used group-aware holdout partitioning to reduce geographic overlap between training records and held-out evaluation records.

Model-specific tokenization was retained so that each architecture received an input representation appropriate to its processing approach. The researchers evaluated precision, recall, F1-score, accuracy and ROC-AUC, while also measuring inference latency to assess computational efficiency. Statistical uncertainty for the main accuracy results was reported using 95% confidence intervals. The comparison therefore examined both predictive performance and processing speed across configurations designed for a common address-extraction task.

At full data availability, ELECTRA combined the highest reported accuracy with substantially lower measured inference latency than BERT, although the supplied research summary does not state the exact latency values. WordCNN and WordLSTM were identified as computationally lighter substitutes when faster processing was more important than achieving the highest predictive performance. The reported results therefore distinguish the strongest accuracy result from alternatives that impose lower computational demands, rather than presenting ELECTRA as the only suitable model in every deployment setting.

This was a controlled comparison of complete neural-model configurations, not an evaluation of isolated network components or a demonstration of general superiority across all business-process automation tasks. Its scope was token-level extraction of four address fields from one aerospace-supplier dataset and one public-schools dataset. The supplied summary does not quantify downstream effects such as delivery speed, data-cleaning costs, improvements in record validity or financial returns. The defensible conclusion is narrower: on these two datasets and under the study’s specified conditions, ELECTRA produced the highest reported full-data accuracy, while convolutional and recurrent models remained alternatives for settings in which computational efficiency was prioritized.

Subject of Research: Comparative benchmarking of neural models for token-level address extraction in business-process automation

Article Title: From NER to Business Process Automation: comparative evaluation of CNN, LSTM, and transformer models for address intelligence

Article References: Jalodia, V., Srivastava, D., & Srivastava, S. K. (2026). From NER to Business Process Automation: comparative evaluation of CNN, LSTM, and transformer models for address intelligence. Knowledge and Information Systems, 68(1), Article 292. https://doi.org/10.1007/s10115-026-02901-3

Image Credits: AI Generated

DOI: 10.1007/s10115-026-02901-3

Keywords: ELECTRA, BERT, address extraction, named entity recognition, business process automation, neural architectures, token classification, inference latency, OASIS dataset, public schools dataset, Business, Process