Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Promptner: Prompting for named entity recognition
Information extraction in low-resource scenarios: Survey and perspective
Information Extraction (IE) seeks to derive structured information from unstructured texts,
often encountering obstacles in low-resource scenarios due to data scarcity and unseen …
often encountering obstacles in low-resource scenarios due to data scarcity and unseen …
2INER: instructive and in-context learning on few-shot named entity recognition
Prompt-based learning has emerged as a powerful technique in natural language
processing (NLP) due to its ability to leverage pre-training knowledge for downstream few …
processing (NLP) due to its ability to leverage pre-training knowledge for downstream few …
Learning semantic proxies from visual prompts for parameter-efficient fine-tuning in deep metric learning
Deep Metric Learning (DML) has long attracted the attention of the machine learning
community as a key objective. Existing solutions concentrate on fine-tuning the pre-trained …
community as a key objective. Existing solutions concentrate on fine-tuning the pre-trained …
Integrating prompt techniques and multi-similarity matching for named entity recognition in low-resource settings
J Yang, L Yao, T Zhang, CY Tsai, Y Lu… - Engineering Applications of …, 2025 - Elsevier
Abstract Few-shot Named Entity Recognition (few-shot NER) is a technique that effectively
trains models with limited annotated data, aiming to address the issue of low accuracy in …
trains models with limited annotated data, aiming to address the issue of low accuracy in …