SRL論文：Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
論文發表：ACL 2018 Github： https://github.com/luheng/lsgn 論文研究對象： 文章研究的是nlp中的語義角色標註問題（srl）。語義角色標註常見方法是基於成分句法樹或基於依存句法樹，今天講的是深度學習模型進行語義角色標註。srl模型可以分爲end to end和gold predicates，即句子中的謂語需要模型進行預測和提前知道兩種情況
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相關文章
- 1. 關於SRL（semantic role label；Semantic Role Labeling）語義角色標註標籤的含義
- 2. 語義角色標註《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
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- 9. 【論文閱讀】- 2017-A Convolutional Neural Network-Based 3D Semantic Labeling Method for ALS Point Clouds
- 10. 論文閱讀《NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE》