Yuxuan Yao
ABSTRACT
We present CNER-UAV, a fine-grained C hinese N ame E ntity R ecognition dataset specifically designed for the task of address resolution in U nmanned A erial V ehicle delivery systems. The dataset encompasses a diverse range of five categories, enabling comprehensive training and evaluation of NER models. To construct this dataset, we sourced the data from a real-world UAV delivery system and conducted a rigorous data cleaning and desensitization process to ensure privacy and data integrity. The resulting dataset, consisting of around 12,000 annotated samples, underwent human experts and L arge L anguage M odel annotation. We evaluated classical NER models on our dataset and provided in-depth analysis. The dataset and models are publicly available at https://github.com/zhhvvv/CNER-UAV.
Supplemental Material
- Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems , Vol. 33 (2020), 1877--1901.Google Scholar
- Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning. 2020. Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555 (2020).Google Scholar
- Yiming Cui, Wanxiang Che, Shijin Wang, and Ting Liu. 2022a. LERT: A Linguistically-motivated Pre-trained Language Model. arxiv: 2211.05344 [cs.CL]Google Scholar
- Yiming Cui, Ziqing Yang, and Ting Liu. 2022b. PERT: Pre-training BERT with Permuted Language Model. arxiv: 2203.06906 [cs.CL]Google Scholar
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).Google Scholar
- Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli. 2023. Chatgpt outperforms crowd-workers for text-annotation tasks. arXiv preprint arXiv:2303.15056 (2023).Google Scholar
- Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016. Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016).Google Scholar
- Jing Li, Aixin Sun, Jianglei Han, and Chenliang Li. 2020. A survey on deep learning for named entity recognition. IEEE Transactions on Knowledge and Data Engineering, Vol. 34, 1 (2020), 50--70.Google ScholarDigital Library
- Onkar Litake, Maithili Ravindra Sabane, Parth Sachin Patil, Aparna Abhijeet Ranade, and Raviraj Joshi. 2022. L3cube-mahaner: A marathi named entity recognition dataset and bert models. In Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference. 29--34.Google Scholar
- Pan Liu, Yanming Guo, Fenglei Wang, and Guohui Li. 2022. Chinese named entity recognition: The state of the art. Neurocomputing , Vol. 473 (2022), 37--53.Google ScholarDigital Library
- Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019).Google Scholar
- Sichun Luo, Bowei He, Haohan Zhao, Yinya Huang, Aojun Zhou, Zongpeng Li, Yuanzhang Xiao, Mingjie Zhan, and Linqi Song. 2023. RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation. arXiv preprint arXiv:2312.16018 (2023).Google Scholar
- Sichun Luo, Jiansheng Wang, Aojun Zhou, Li Ma, and Linqi Song. 2024 a. Large Language Models Augmented Rating Prediction in Recommender System. In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 7960--7964. https://doi.org/10.1109/ICASSP48485.2024.10447514Google ScholarCross Ref
- Sichun Luo, Yuxuan Yao, Bowei He, Yinya Huang, Aojun Zhou, Xinyi Zhang, Yuanzhang Xiao, Mingjie Zhan, and Linqi Song. 2024 b. Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation. arXiv preprint arXiv:2401.13870 (2024).Google Scholar
- Meituan. 2021. Food Delivery Giant Meituan Unveils Drones for Delivery Service, Offering New User Experience. https://pandaily.com/food-delivery-giant-meituan-unveils-drones-for-delivery-service/Google Scholar
- Ke Wang, Houxing Ren, Aojun Zhou, Zimu Lu, Sichun Luo, Weikang Shi, Renrui Zhang, Linqi Song, Mingjie Zhan, and Hongsheng Li. 2023. Mathcoder: Seamless code integration in llms for enhanced mathematical reasoning. arXiv preprint arXiv:2310.03731 (2023).Google Scholar
- Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, and Michael Zeng. 2021. Want to reduce labeling cost? GPT-3 can help. arXiv preprint arXiv:2108.13487 (2021).Google Scholar
- Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems , Vol. 32 (2019).Google Scholar
- Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, et al. 2022. Glm-130b: An open bilingual pre-trained model. arXiv preprint arXiv:2210.02414 (2022).Google Scholar
- Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019. ERNIE: Enhanced language representation with informative entities. arXiv preprint arXiv:1905.07129 (2019).Google Scholar
- Aojun Zhou, Ke Wang, Zimu Lu, Weikang Shi, Sichun Luo, Zipeng Qin, Shaoqing Lu, Anya Jia, Linqi Song, Mingjie Zhan, et al. 2023. Solving challenging math word problems using gpt-4 code interpreter with code-based self-verification. arXiv preprint arXiv:2308.07921 (2023).Google Scholar
Index Terms
- Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery
Recommendations
NumER: A Fine-Grained Numeral Entity Recognition Dataset
Natural Language Processing and Information SystemsAbstractNamed entity recognition (NER) is essential and widely used in natural language processing tasks such as question answering, entity linking, and text summarization. However, most current NER models and datasets focus more on words than on ...
Fine-grained Dutch named entity recognition
This paper describes the creation of a fine-grained named entity annotation scheme and corpus for Dutch, and experiments on automatic main type and subtype named entity recognition. We give an overview of existing named entity annotation schemes, and ...
EduNER: a Chinese named entity recognition dataset for education research
AbstractA high-quality domain-oriented dataset is crucial for the domain-specific named entity recognition (NER) task. In this study, we introduce a novel education-oriented Chinese NER dataset (EduNER). To provide representative and diverse training data,...
Comments