Computer Science > Computation and Language
arXiv:2403.06097 (cs)
[Submitted on 10 Mar 2024 (v1), last revised 19 Mar 2024 (this version, v2)]
Title:Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery
View a PDF of the paper titled Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery, by Yuxuan Yao and 4 other authors
View PDFHTML (experimental)Abstract:We present CNER-UAV, a fine-grained \textbf{C}hinese \textbf{N}ame \textbf{E}ntity \textbf{R}ecognition dataset specifically designed for the task of address resolution in \textbf{U}nmanned \textbf{A}erial \textbf{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 \textbf{L}arge \textbf{L}anguage \textbf{M}odel annotation. We evaluated classical NER models on our dataset and provided in-depth analysis. The dataset and models are publicly available at \url{this https URL}.
| Comments: | Accepted by TheWebConf'24 (WWW'24) as a Resource Paper |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2403.06097 [cs.CL] |
| (orarXiv:2403.06097v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2403.06097 arXiv-issued DOI via DataCite |
Submission history
From: Sichun Luo [view email][v1] Sun, 10 Mar 2024 05:12:16 UTC (325 KB)
[v2] Tue, 19 Mar 2024 11:36:26 UTC (325 KB)
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View a PDF of the paper titled Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery, by Yuxuan Yao and 4 other authors
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