<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[Ahighmore的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/Ahighmore</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; Ahighmore]]></copyright><item><title><![CDATA[通过BERT-Based标签和实例嵌入提升远程监督的RE]]></title><link>https://blog.youkuaiyun.com/Ahighmore/article/details/119867622</link><guid>https://blog.youkuaiyun.com/Ahighmore/article/details/119867622</guid><author>Ahighmore</author><pubDate>Tue, 24 Aug 2021 18:15:41 +0800</pubDate><description><![CDATA[Improving Distantly-supervised Relation Extraction Through BERT-Base label and Instance Embedding

文章目录摘要一、REDSandT二、句子编码器1.输入表示2. 输入嵌入3.句子表示三、bag编码器四、training

摘要
DS_RE是将RE扩展到大型语料库的有效方法，但会收到标签噪声影响。How to solve? 现在方法是试图通过多实例学习和提供额外的信息来减轻噪音，但识别最频繁的关系，会忽略长尾关]]></description><category></category></item><item><title><![CDATA[【论文学习E2GRE】Entity and Evidence Guided Relation Extraction for DocRED2020]]></title><link>https://blog.youkuaiyun.com/Ahighmore/article/details/119578768</link><guid>https://blog.youkuaiyun.com/Ahighmore/article/details/119578768</guid><author>Ahighmore</author><pubDate>Tue, 10 Aug 2021 21:58:57 +0800</pubDate><description><![CDATA[文章目录摘要一、介绍二、方法1.如何生成实体引导的输入2.实体引导的RE3.entity and evidence-guided joint training for RE1)Evidence Prediction2)Evidence-guided Fine-tuning with BERT Attention Probabilities3)Joint Training with Evidence Prediction三、ablation study

论文地址：https://arxiv.org/pd..]]></description><category></category></item><item><title><![CDATA[【论文阅读GAIN】Double Graph Based Reasoning for Document-level Relation Extraction[EMNLP2020]]]></title><link>https://blog.youkuaiyun.com/Ahighmore/article/details/119545199</link><guid>https://blog.youkuaiyun.com/Ahighmore/article/details/119545199</guid><author>Ahighmore</author><pubDate>Mon, 09 Aug 2021 20:34:45 +0800</pubDate><description><![CDATA[提示：文章写完后，目录可以自动生成，如何生成可参考右边的帮助文档

文章目录摘要一、面临问题3. 路径推理机制二、Task Formulation三、GAIN1.编码模块2.提及聚合模块hMG3.实体级推理模块EG4.分类模块

摘要
具有双图特征的图聚合和推理网络。
GAIN 首先构建一个提及级别的图（hMG)来表示不同提及之间的复杂交互；
还有一个实体级别图（EG），相同实体的潜在提及；
然后在此基础上提出一个路径推理机制来推断实体之间的关系。


GAIN可以聚合文档感知上下文信息并推断文档的逻辑关系]]></description><category></category></item><item><title><![CDATA[【论文学习SSAN2021】Entity Structure Within and Throughout]]></title><link>https://blog.youkuaiyun.com/Ahighmore/article/details/119452326</link><guid>https://blog.youkuaiyun.com/Ahighmore/article/details/119452326</guid><author>Ahighmore</author><pubDate>Fri, 06 Aug 2021 18:19:15 +0800</pubDate><description><![CDATA[Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction

文章目录Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction创新点一、pandas是什么？二、使用步骤1.引入库2.读入数据总结]]></description><category></category></item><item><title><![CDATA[after train]]></title><link>https://blog.youkuaiyun.com/Ahighmore/article/details/119451992</link><guid>https://blog.youkuaiyun.com/Ahighmore/article/details/119451992</guid><author>Ahighmore</author><pubDate>Fri, 06 Aug 2021 13:24:54 +0800</pubDate><description><![CDATA[dataset -   doc: Villanova Preparatory School is an Augustinian Catholic co - ed day and boarding school in the United States , located in the California town of Ojai . Sitting on more than 130 acres , Villanova 's campus has many athletic facilities , two]]></description><category></category></item><item><title><![CDATA[【论文学习DocuNet】Document-level Relation Extraction as Semantic Segmentation（2021）]]></title><link>https://blog.youkuaiyun.com/Ahighmore/article/details/119414002</link><guid>https://blog.youkuaiyun.com/Ahighmore/article/details/119414002</guid><author>Ahighmore</author><pubDate>Thu, 05 Aug 2021 14:11:36 +0800</pubDate><description><![CDATA[【论文学习】Document-level Relation Extraction as Semantic Segmentation（2021）
文章目录前言一、简介二、Methodology方法1. Preliminary 初步2.Encoder Module3.U-shaped Segmentation Module4.分类模块总结

前言
提示：通过预测实体级关系矩阵来捕捉局部和全局信息，与计算机视觉中的语义分割任务并行。
创新点：.提出一个U型网络，编码器来捕捉实体的上下文信息，再利用U型分割模块来捕]]></description><category></category></item><item><title><![CDATA[【论文学习ATLOP】Document-Level Relation Extraction]]></title><link>https://blog.youkuaiyun.com/Ahighmore/article/details/119389269</link><guid>https://blog.youkuaiyun.com/Ahighmore/article/details/119389269</guid><author>Ahighmore</author><pubDate>Wed, 04 Aug 2021 19:27:44 +0800</pubDate><description><![CDATA[欢迎使用Markdown编辑器
你好！ 这是你第一次使用 Markdown编辑器 所展示的欢迎页。如果你想学习如何使用Markdown编辑器, 可以仔细阅读这篇文章，了解一下Markdown的基本语法知识。
新的改变
我们对Markdown编辑器进行了一些功能拓展与语法支持，除了标准的Markdown编辑器功能，我们增加了如下几点新功能，帮助你用它写博客：

全新的界面设计 ，将会带来全新的写作体验；
在创作中心设置你喜爱的代码高亮样式，Markdown 将代码片显示选择的高亮样式 进行展示；
增加了 图片]]></description><category></category></item></channel></rss>