1.采用多示例学习缓解噪声 将同一Bag(相同实体对)置信度最高的句子的关系 作为Bag中所有句子的关系(假设还是很强) 2.提出PCNNs网络 之前的CNN只能提取到局部信息,由卷积核决定,所以本文中将同一个句子分为三段,然后分别池化,最终的效果证明确实提升。 考虑原因的话,句子中的词语距离两个实体的距离是重要的。 多...
论文笔记:Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks,程序员大本营,技术文章内容聚合第一站。
论文解读 Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks. 任务:关系抽取 来源:EMNLP 2015 引言: 这一篇论文的工作是在Zeng 2014基础上的扩展(Zeng那篇论文的解析https://www.jianshu.com/p/f4a9c2fd733c),从Fully Supervised 到Distant Supervised。 这一篇论文使用的模型...
Piecewise Convolutional Neural Networks(PCNNs)模型 PCNNs模型由Zeng et al.于2015提出,主要针对两个问题提出解决方案: 针对远程监督的wrong label problem,该模型提出采用多示例学习的方式从训练集中抽取取置信度高的训练样例训练模型。 针对传统统计模型特征抽取过程中出现的错误和后续的错误传播问题,该模型提出用 pi...
Existing convolutional neural networks extract important features of sentences without local features or the feature sequence. Thus, these models do not perform well, especially for transition sentences. To this end, we propose a Piecewise Pooling Convolutional Neural Network (PPCNN) for sentiment ...
PCNNs全名为Piecewise Convolutional Neural Networks,包含两层含义:Piecewise max pooling layer和Convolutional Neural Networds,对应到最大池化层和卷积层。用卷积神经网络强大的特征提取功能,能自动抽取丰富的特征,并且减少人工设计特征和NLP工具库抽取特征带来的误差。省时省力又能减少误差,何乐不为。
[论文研读]Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks,程序员大本营,技术文章内容聚合第一站。
论文笔记:Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks 背景知识:Distant Supervised Relation Extraction 该方法由 M Mintz 于 ACL2009 上首次提出,与传统预先定义关系类别不同,Distant Supervision通过将知识库与非结构化文本对齐来自动构建大量训练数据,减少模型对人工标注数据的...
Architecture of recurrent piecewise convolutional neural networks (RPCNN) for multi-instance learning Full size image Input layer Given a candidate, the corresponding multiple instances I0, I1,…, Im are arranged in descending order according to the length of context between the two entity mentions...
FPGA-accelerated deep convolutional neural networks for high throughput and energy efficiency. Concurr. Comput.Pract. Exper. 29, e3850 (2017). Article Google Scholar Dua, D. & Graff, C. UCI machine learning repository. UCI http://archive.ics.uci.edu/ml (2017). LeCun, Y. et al. ...