The paper "Few-shot Name Entity Recognition on StackOverflow" addresses the challenge of named entity recognition (NER) in the vast and limited labeled data of StackOverflow. The authors propose a few-shot NER method called RoBERTa+MAML, which leverages meta-learning to improve entity recognition accuracy with minimal annotated training data. Evaluations on the StackOverflow NER corpus (27 entity types) show a 5% F1 score improvement over the baseline model. The method is further enhanced by domain-specific phrase processing and knowledge-based pattern extraction, leading to even better results. The paper also discusses related works, introduces prompt learning and meta-learning techniques, and provides experimental results demonstrating the effectiveness of the proposed approach. The authors conclude that meta-learning, domain-specific phrase processing, and knowledge-based patterns can significantly benefit future software-related information extraction and question-answering tasks.The paper "Few-shot Name Entity Recognition on StackOverflow" addresses the challenge of named entity recognition (NER) in the vast and limited labeled data of StackOverflow. The authors propose a few-shot NER method called RoBERTa+MAML, which leverages meta-learning to improve entity recognition accuracy with minimal annotated training data. Evaluations on the StackOverflow NER corpus (27 entity types) show a 5% F1 score improvement over the baseline model. The method is further enhanced by domain-specific phrase processing and knowledge-based pattern extraction, leading to even better results. The paper also discusses related works, introduces prompt learning and meta-learning techniques, and provides experimental results demonstrating the effectiveness of the proposed approach. The authors conclude that meta-learning, domain-specific phrase processing, and knowledge-based patterns can significantly benefit future software-related information extraction and question-answering tasks.