ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction

机译：ETH-DS3Lab在SemEval-2018上的任务7：有效地结合递归和卷积神经网络进行关系分类和提取

获取原文

页面导航

摘要
著录项
相似文献
相关主题

摘要

Reliably detecting relevant relations between entities in unstructured text is a valuable resource for knowledge extraction, which is why it has awaken significant interest in the field of Natural Language Processing. In this paper, we present a system lor relation classification and extraction based on an ensemble of convolutional and recurrent neural networks that ranked first in 3 out of the 4 subtasks at Se-mEval 2018 Task 7. We provide detailed explanations and grounds for the design choices behind the most relevant features and analyze their importance.

机译：可靠地检测非结构化文本中实体之间的相关关系是知识提取的宝贵资源，这就是为什么它引起了自然语言处理领域的极大兴趣。在本文中，我们基于卷积神经网络和递归神经网络的整体提出了系统关系分类和提取，在Se-mEval 2018 Task 7的4个子任务中排名第3选择最相关的功能，并分析其重要性。

著录项

来源
《International workshop on semantic evaluation;Annual conference of the North American Chapter of the Association for Computational Linguistics: human language technologies》|2018年|689-696|共8页
会议地点
作者
Jonathan Rotsztejn; Nora Hollenstein; Ce Zhang;
展开▼
作者单位

展开▼
会议组织
原文格式 PDF
正文语种
中图分类
关键词

相似文献

外文文献
中文文献
专利

1. Combining attention-based bidirectional gated recurrent neural network and two-dimensional convolutional neural network for document-level sentiment classification [J] . Liu Fagui, Zheng Jingzhong, Zheng Lailei, Neurocomputing . 2020,第Jana2期

机译：结合基于注意力的双向门控递归神经网络和二维卷积神经网络进行文档级情感分类
2. Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial-temporal features extraction [J] . DAngelo Gianni, Palmieri Francesco Journal of network and computer applications . 2021,第Jana期

机译：网络流量分类使用深卷积复制自动化器神经网络进行空间时间特征提取
3. Multi-task learning model based on recurrent convolutional neural networks for citation sentiment and purpose classification [J] . Yousif Abdallah, Niu Zhendong, Chambua James, Neurocomputing . 2019,第MARa28期

机译：基于递归卷积神经网络的引文情感和目的分类多任务学习模型
4. ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction [C] . Jonathan Rotsztejn, Nora Hollenstein, Ce Zhang International workshop on semantic evaluation . 2018

机译：Eth-DS3LAB在Semeval-2018任务7：有效地结合了反复和卷积神经网络以进行相关分类和提取
5. Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks [D] . Hassan, Abdalraouf. 2018

机译：基于卷积神经网络和递归神经网络的深度神经语言文本分类模型
6. Plus Disease in Retinopathy of Prematurity: Convolutional Neural Network Performance Using a Combined Neural Network and Feature Extraction Approach [O] . Veysi M. Yildiz, Peng Tian, Ilkay Yildiz, -1

机译：早产儿视网膜病变中的疾病加成：使用组合神经网络和特征提取方法的卷积神经网络性能
7. ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction [O] . Jonathan Rotsztejn, Nora Hollenstein, Ce Zhang 2018

机译：Eth-DS3LAB在Semeval-2018任务7：有效地结合了反复和卷积神经网络以进行相关分类和提取

ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction

摘要

著录项

相似文献

相关主题

期刊订阅