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复旦大学 《知识图谱：概念与技术》 课件（1）
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Ch01-1知识图谱的基本概念.pdf
Ch01-2知识图谱的基本概念.pdf
Ch01-3知识图谱的基本概念.pdf
Ch02 基础知识.pdf
Ch03词汇挖掘与实体挖掘.pdf
Ch04关系抽取.pdf
Ch05概念图谱构建.pdf
Ch06百科图谱构建.pdf
Ch07知识图谱的众包构建.pdf
Ch08知识图谱的质量控制.pdf
Ch09知识图谱的建模与存储.pdf
Ch10知识图谱的查询与检索.pdf
Ch11图数据管理系统.pdf
Ch12基于知识图谱的语言认知.pdf
Ch13基于知识图谱的搜索与推荐.pdf
Ch14基于知识图谱的问答.pdf
Ch15知识图谱实践.pdf
Ch16开放性问题.pdf
参考文献-2021.zip
知识图谱作业-2021.pptx
知识图谱期末作业-2021.zip
参考文献：
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification.pdf
Autoprompt Eliciting knowledge from language models with automatically generated prompts.pdf
Calibrate Before Use Improving Few-Shot Performance of Language Models..pdf
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference.pdf
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference.pdf
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer..pdf
GPT Understands, Too.pdf
How Can We Know What Language Models Know.pdf
Improving and Simplifying Pattern Exploiting Training.pdf
It is Not Just Size That Matters Small Language Models Are Also Few-Shot Learners..pdf
Language Models as Knowledge Bases.pdf
Learning How to Ask Querying LMs with Mixtures of Soft Prompts.pdf
Parameter-Efficient Transfer Learning for NLP.pdf
Pre-train, Prompt, and Predict A Systematic Survey of.pdf
Prefix-tuning Optimizing continuous prompts for generation.pdf
Prompt Programming for Large Language Models Beyond the Few-Shot Paradigm.pdf
The Power of Scale for Parameter-Efficient Prompt Tuning.pdf
What Makes Good In-Context Examples for GPT-3.pdf
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