SRL论文：Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling

时间 2020-12-30

标签 srl 繁體版

原文原文链接

论文发表：ACL 2018 Github： https://github.com/luheng/lsgn 论文研究对象：文章研究的是nlp中的语义角色标注问题（srl）。语义角色标注常见方法是基于成分句法树或基于依存句法树，今天讲的是深度学习模型进行语义角色标注。srl模型可以分为end to end和gold predicates，即句子中的谓语需要模型进行预测和提前知道两种情况

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1. 关于SRL（semantic role label；Semantic Role Labeling）语义角色标注标签的含义
2. 语义角色标注《Deep Semantic Role Labeling？What Works and What’s Next》
3. 00040-Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional
4. Neural machine translation by jointly learning to align and translate论文阅读笔记
5. 论文 | Predicting Mortgage Default Using Convolutional Neural Networks
6. 【论文阅读】Neural Machine Translation By Jointly Learning To Align and Translate
7. Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale... 论文笔记
8. attention model--Neural machine translation by jointly learning to align and translate论文解读
9. 【论文阅读】- 2017-A Convolutional Neural Network-Based 3D Semantic Labeling Method for ALS Point Clouds
10. 论文阅读《NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE》

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