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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 fromnatural language, on which pre-trained models are trained; web queries areshort and lack contextual information; and labeled data for NER is scarce. Wepropose DeepTagger, a knowledge-enhanced NER model for web-based ads queries.The proposed knowledge enhancement framework leverages both model-free andmodel-based approaches. For model-free enhancement, we collect unlabeled webqueries to augment domain knowledge; and we collect web search results toenrich the information of ads queries. We further leverage effective promptingmethods to automatically generate labels using large language models such asChatGPT. Additionally, we adopt a model-based knowledge enhancement methodbased on adversarial data augmentation. We employ a three-stage trainingframework to train DeepTagger models. Empirical results in various NER tasksdemonstrate the effectiveness of the proposed framework.

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