add some dataset

Author: Accagain2014

README.md

@@ -8,6 +8,9 @@ Understanding the Application about Deep Learning in Text Matching Area & Implem
 - [Richard Socher](http://www.socher.org/index.php/Main/HomePage)
 - [Hang Li](http://www.hangli-hl.com/index.html)
 
+## Survey
+> [深度文本匹配综述](http://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFQ&dbname=CAPJLAST&filename=JSJX20160920002&uid=WEEvREcwSlJHSldRa1FhdXNXYXJvK0FZMlhXUDZsYnBMQjhHTElMeE1jRT0=$9A4hF_YAuvQ5obgVAqNKPCYcEjKensW4ggI8Fm4gTkoUKaID8j8gFw!!&v=MzA2OTFscVdNMENMTDdSN3FlWU9ac0ZDcmxWYnZPSTFzPUx6N0Jkckc0SDlmTXBvMUZaT3NOWXc5TXptUm42ajU3VDNm)
+<br> 
 
 
 ## Methods & Papers
@@ -19,7 +22,7 @@ Understanding the Application about Deep Learning in Text Matching Area & Implem
 -----
 > [**CDSSM**]() 
  <br> [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
+ <br> WWW 2014, word hash + CNN + DNN
 ----
 
 > [**CLSM**]() 
@@ -47,7 +50,8 @@ for Matching Natural Language Sentences](https://papers.nips.cc/paper/5550-convo
 <br> [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
+<br> (D)CNN+MLP(tensor layer); 
+<br> 基于语义表达的结构
 
 
 ## Related talks and papers
@@ -60,3 +64,26 @@ Natural Language Processing](https://www.microsoft.com/en-us/research/wp-content
 > [DSSM/Sent2Vec Release Version](https://www.microsoft.com/en-us/download/details.aspx?id=52365)
 <br> MSRA发布的Sent2Vec发行版
 
+## Datasets
+* [Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks](http://arxiv.org/abs/1502.05698 "Jason Weston, Antoine Bordes, Sumit Chopra, Tomas Mikolov, Alexander M. Rush") ([fb.ai/babi](http://fb.ai/babi))
+* [Teaching Machines to Read and Comprehend](http://arxiv.org/abs/1506.03340 "Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom") ([github.com/deepmind/rc-data](https://github.com/deepmind/rc-data))
+* [One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling](http://arxiv.org/abs/1312.3005 "Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, Tony Robinson") ([github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark))
+* [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](http://arxiv.org/abs/1506.08909 "Ryan Lowe, Nissan Pow, Iulian Serban, Joelle Pineau") ([cs.mcgill.ca/~jpineau/datasets/ubuntu-corpus-1.0](http://cs.mcgill.ca/~jpineau/datasets/ubuntu-corpus-1.0/))
+* [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](http://arxiv.org/abs/1506.06724 "Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler") ([BookCorpus](http://www.cs.toronto.edu/~mbweb/))
+* [Every publicly available Reddit comment, for research.](https://www.reddit.com/r/datasets/comments/3bxlg7/i_have_every_publicly_available_reddit_comment/ "Stuck_In_the_Matrix")
+* [Stack Exchange Data Dump](https://archive.org/details/stackexchange "Stack Exchange")
+* [Europarl: A Parallel Corpus for Statistical Machine Translation](http://www.iccs.inf.ed.ac.uk/~pkoehn/publications/europarl-mtsummit05.pdf "Philipp Koehn") ([www.statmt.org/europarl/](http://www.statmt.org/europarl/))
+* [RTE Knowledge Resources](http://aclweb.org/aclwiki/index.php?title=RTE_Knowledge_Resources)
+
+
+## Pretrained Models
+* [Model Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo "Berkeley Vision and Learning Center")
+* [word2vec](https://code.google.com/p/word2vec/ "Tomas Mikolov")
+ * [GoogleNews-vectors-negative300.bin.gz](https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing)
+ * [freebase-vectors-skipgram1000.bin.gz](https://docs.google.com/file/d/0B7XkCwpI5KDYaDBDQm1tZGNDRHc/edit?usp=sharing)
+* [GloVe](http://nlp.stanford.edu/projects/glove/ "Jeffrey Pennington, Richard Socher, Christopher D. Manning")
+* [SENNA](http://ronan.collobert.com/senna/ "R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, P. Kuksa")
+
+
+## References
+https://github.com/robertsdionne/neural-network-papers/blob/master/README.md

helper/game.py

@@ -0,0 +1,48 @@
+#coding=utf-8
+
+ans = []
+def find_path(maze):
+ dir = [[-1, 0], [0, 1], [1, 0], [0, -1]]
+ hav = [ [0] * 5 for _ in range(5) ]
+ global ans
+ ans = []
+ resutl = 0
+
+ def next(x, y, num):
+
+ if num == 24:
+ resutl = 1
+ print 'Found'
+ return
+ for one in dir:
+ x_ = x + one[0]
+ y_ = y + one[1]
+ if (x_>=0 and x_<5) and (y_ >=0 and y_ < 5):
+ if not hav[x_][y_] and maze[x_][y_] == 1:
+ global ans
+ ans.append((x_, y_))
+ hav[x_][y_] = 1
+ next(x_, y_, num+1)
+ hav[x_][y_] = 0
+ ans = ans[:-1]
+
+ for i in range(5):
+ for j in range(5):
+ if (i == 0) and (j == 1):
+ continue
+ hav[i][j] = 1
+ next(i, j, 1)
+ ans.append((i, j))
+ if resutl :
+ print ans
+ return
+ ans = []
+ hav[i][j] = 0
+
+
+
+
+if __name__ == '__main__':
+ map = [ [1] * 5 for _ in range(5)]
+ map[0][1] = 0
+ find_path(map)