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这篇博客主要记录一下阅读过的重要的文本风格转换的paper,可能不会非常详细,但是会记录核心内容,遇到自己非常喜欢的或者真的特别新颖的,可能会详细的写吧,主要是paper太多了,全部详细的写没时间~

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接着上一篇博客,这篇博客也是讲解图神经网络在Document-level关系抽取上的应用,共讲解4篇论文:《Global-to-Local Neural Networks for Document-Level Relation Extraction》、《HIN: Hierarchical Inference Network for Document-Level Relation Extraction》、《Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling》、《Entity and Evidence Guided Relation Extraction for DocRED》。

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这篇博客主要是讲解图神经网络在Document-level关系抽取上的应用,共讲解4篇论文:《Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network》、《Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs》、《Reasoning with Latent Structure Refinement for Document-Level Relation Extraction》、《Double Graph Based Reasoning for Document-level Relation Extraction》。

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这篇博客主要讲解图神经网络在sentence-level的关系抽取任务中的应用,共讲解三篇paper:《Graph Neural Networks with Generated Parameters for Relation Extraction》、《GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction》、《A General Framework for Information Extraction using Dynamic Span Graphs》。

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这篇博客主要讲解一下Document-level 关系抽取上的最新的两个数据集:DocRED以及SCIREX,paper名称:《DocRED: A Large-Scale Document-Level Relation Extraction Dataset》、《SCIREX: A Challenge Dataset for Document-Level Information Extraction》。

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近期会更新一系列NER的paper解读,计划2周时间将NER的重要的论文刷完,有一个想做的事情嘻嘻😁。这篇博客主要解读一下目前多模态NER上质量比较好的论文:《Adaptive Co-Attention Network for Named Entity Recognition in Tweets》、《Visual Attention Model for Name Tagging in Multimodal Social Media》、《Multimodal Named Entity Recognition for Short Social Media Posts》、《Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer》。

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