DeepTagger: Knowledge Enhanced Named Entity Recognition for Web-Based Ads Queries

Simiao Zuo, Pengfei Tang, Xinyu Hu, Qiang Lou, Jian Jiao, Denis X Charles

Abstract:

Named entity recognition (NER) is a crucial task for online advertisement. State-of-the-art solutions leverage pre-trained language models for this task. However, three major challenges
remain unresolved: web queries differ from natural language, on which pre-trained models are
trained; web queries are short and lack contextual information; and labeled data for NER is
scarce. We propose DeepTagger, a knowledge-enhanced NER model for web-based ads queries.
The proposed knowledge enhancement framework leverages both model-free and model-based
approaches. For model-free enhancement, we collect unlabeled web queries to augment domain knowledge; and we collect web search results to enrich the information of ads queries. We
further leverage effective prompting methods to automatically generate labels using large language models such as ChatGPT. Additionally, we adopt a model-based knowledge enhancement
method based on adversarial data augmentation. We employ a three-stage training framework to
train DeepTagger models. Empirical results in various NER tasks demonstrate the effectiveness
of the proposed framework.