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arXiv · 2508.19372

Database Entity Recognition with Data Augmentation and Deep Learning

Zikun Fu·Chen Yang·Kourosh Davoudi·Ken Q. Pu
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Abstract

This paper addresses the challenge of Database Entity Recognition (DB-ER) in Natural Language Queries (NLQ). We present several key contributions to advance this field: (1) a human-annotated benchmark for DB-ER task, derived from popular text-to-sql benchmarks, (2) a novel data augmentation procedure that leverages automatic annotation of NLQs based on the corresponding SQL queries which are available in popular text-to-SQL benchmarks, (3) a specialized language model based entity recognition model using T5 as a backbone and two down-stream DB-ER tasks: sequence tagging and token classification for fine-tuning of backend and performing DB-ER respectively. We compared our DB-ER tagger with two state-of-the-art NER taggers, and observed better performance in both precision and recall for our model. The ablation evaluation shows that data augmentation boosts precision and recall by over 10%, while fine-tuning of the T5 backbone boosts these metrics by 5-10%.

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Zikun Fu, Chen Yang, Kourosh Davoudi, Ken Q. Pu. 2025-08-26. Database Entity Recognition with Data Augmentation and Deep Learning. https://arxiv.org/abs/2508.19372

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