基于数据增强与深度学习的数据库实体识别
Database Entity Recognition with Data Augmentation and Deep Learning
AI总结：
本文提出基于T5骨干网络与数据增强的数据库实体识别方法，通过序列标注和令牌分类双任务微调，在精确率和召回率上优于现有NER模型。
摘要：
本文针对自然语言查询（NLQ）中的数据库实体识别（DB-ER）挑战展开研究。我们提出了几项关键贡献以推动该领域发展：（1）一个源自流行文本到SQL基准的人工标注DB-ER任务基准；（2）一种新颖的数据增强流程，利用流行文本到SQL基准中可用的对应SQL查询对NLQ进行自动标注；（3）一个专门的基于语言模型的实体识别模型，以T5为骨干网络，并设计两个下游DB-ER任务：序列标注和令牌分类，分别用于骨干网络的微调和执行DB-ER。我们将所提出的DB-ER标注器与两个最先进的NER标注器进行了比较，观察到我们的模型在精确率和召回率上均表现更优。消融评估表明，数据增强使精确率和召回率提升超过10%，而T5骨干网络的微调使这些指标提升5-10%。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
