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

arXiv: https://arxiv.org/abs/2306.17413 · HTML: https://mlsanity.com/paper/2306.17413/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2023.

## Built on by (most cited first, 3 of 3)

- [Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing](https://mlsanity.com/paper/2310.13855/) (2023)
- [Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems](https://mlsanity.com/paper/2509.09690/) (2025)
- [A Unified Structured Query Understanding Framework for Industrial Semantic Search](https://mlsanity.com/paper/2605.27441/) (2026)

## Abstract (arXiv)

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.