中文短文本分类实例十四-LEAM（Joint Embedding of Words and Labels for Text Classification）
一.概述python LEAM（Joint Embedding of Words and Labels for Text Classification），是Guoyin Wang等提出的一种文本分类新方法，看论文标题就能够发现，该方法主要是构建 "类别标签 (label) 与词向量 (word-embedding)的联合嵌入"，使用注意力机制 (Attention) 做为 laeb
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相关文章
- 1. A NEW METHOD OF REGION EMBEDDING FOR TEXT CLASSIFICATION
- 2. 【论文笔记】Embedding of Embedding (EOE) : Joint Embedding for Coupled Heterogeneous Networks
- 3. Text-CNN-文本分类-keras
- 4. (28)[AISTATS15] Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing
- 5. Bag of Tricks for Efficient Text Classification
- 6. 「Keras」Demonstrates the Use of Convolution1D for Text Classification
- 7. 论文笔记-recurrent CNN for text classification
- 8. 《Character-level Convolutional Networks for Text Classification》
- 9. Text Classification
- 10. Week1.3 Simple deep learning for text classification