In such methods, recognition is understood as answering questions about named entities. The system must return valid named entities of the required type that are referenced in the sentence. In particular, we study the effect of prompts on the nested named entity recognition in few-shot setting. ...
在此示例中,使用标准提示技术来确保模型生成产品评论,角色提示技术用于确保评论是从技术专家的角度撰写的,使用种子词提示技术来确保评论集中在笔记本电脑的强大功能上。 第五章:零、单个和小样本提示(Zero, One and Few Shot Prompting) 零提示、单个提示和小样本提示是用于从ChatGPT中生成文本的技术,只有极少或没有...
Dai X, Adel H (2020) An analysis of simple data augmentation for named entity recognition. In: Proceedings of the 28th international conference on computational linguistics, pp 3861–3867 Kobayashi S (2018) Contextual augmentation: data augmentation by words with paradigmatic relations. In: Proceeding...
In today’s blog post, I am introducing one of the latest GPTs and Assistants I created, named HumbleAI. I will let the models explain it by answering a few questions. For each question, I’ve selected a response I liked or found worthy of sharing. At the end of the post, you can...
In Uyghur triplet extraction, entities and suffixes are not handled like Uyghur root extraction because entities may contain more than one word, so we have to consider the suffix of the last word in the entity. The Zero-Shot relation triple extraction dataset, known as Zero-Shot-Prompt-RE, ...