complete DSSM

Author: Accagain2014

README.md

@@ -12,33 +12,33 @@ Understanding the Application about Deep Learning in Text Matching Area & Implem
 
 ## Methods & Papers
 
-- > [**DSSM**](./DSSM/dssm.py)
+> [**DSSM**](./DSSM/dssm.py)
 [Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://posenhuang.github.io/papers/cikm2013_DSSM_fullversion.pdf)
 <br> CIKM 2013
 <br> 词袋模型,基于语义表达的结构, word hash + DNN 
 
-- > [**CDSSM**]() [Learning Semantic Representations Using Convolutional Neural Networks for Web Search](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/www2014_cdssm_p07.pdf)
+> [**CDSSM**]() [Learning Semantic Representations Using Convolutional Neural Networks for Web Search](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/www2014_cdssm_p07.pdf)
  <br> WWW 2014
  
-- > [**CLSM**]() [A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2014_cdssm_final.pdf)
+> [**CLSM**]() [A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2014_cdssm_final.pdf)
  <br> CIKM 2014
  <br> 基于匹配的结构, word hash + CNN, CLSM和C-DSSM有什么区别呢
  
-- > [**DSSM的应用**]() [Modeling Interestingness with Deep Neural Networks](https://www.microsoft.com/en-us/research/wp-content/uploads/2014/10/604_Paper.pdf)
+> [**DSSM的应用**]() [Modeling Interestingness with Deep Neural Networks](https://www.microsoft.com/en-us/research/wp-content/uploads/2014/10/604_Paper.pdf)
 <br> EMNLP 2014
 <br> DSSM应用于文本分析，在automatic highlighting和contextual entity search问题上效果好。
 <br> 主要有两点贡献：
 <br> 1) DSSM + CNN
 <br> 2) 不针对相关性，加了一个ranker
 
 
-- > [**ARC-I/ARC-II**]() 
+> [**ARC-I/ARC-II**]() 
  [Convolutional Neural Network Architectures 
 for Matching Natural Language Sentences](https://papers.nips.cc/paper/5550-convolutional-neural-network-architectures-for-matching-natural-language-sentences.pdf)
 <br> NIPS 2014
 <br> CNN的基于语义表达和基于匹配的两种结构; 增加了门解决句子长度不一致问题
 
-- > [**CNTN**]() [Convolutional Neural Tensor Network 
+> [**CNTN**]() [Convolutional Neural Tensor Network 
 Architecture for Community-based Question Answering](https://ijcai.org/Proceedings/15/Papers/188.pdf)
 <br> IJCAI 2015
 <br> (D)CNN+MLP(tensor layer); 基于语义表达的结构