Database Entity Recognition with Data Augmentation and Deep Learning

• URL: http://arxiv.org/abs/2508.19372v1
• Date: Tue, 26 Aug 2025 19:05:40 GMT
• Title: Database Entity Recognition with Data Augmentation and Deep Learning
• Authors: Zikun Fu, Chen Yang, Kourosh Davoudi, Ken Q. Pu,
• Abstract summary: We present a human-annotated benchmark for DB-ER task, derived from popular text-to-sql benchmarks.<n>We also present a novel data augmentation procedure that leverages automatic annotation of NLQs.
• Score: 2.7396543860784788
• License: http://creativecommons.org/licenses/by/4.0/
• 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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