example/cnn_text_classification/README.md - mxnet-test - Git at Google

 Implementing CNN for Text Classification in MXNet
 ============
 It is slightly simplified implementation of Kim's [Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1408.5882) paper in MXNet.

 Recently, I have been learning mxnet for Natural Language Processing (NLP). I followed this nice blog ["Implementing a CNN for Text Classification in Tensorflow" blog post.](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/) to reimplement it by mxnet framwork.
 Data preprocessing code and courpus are directly borrowed from original author [cnn-text-classification-tf](https://github.com/dennybritz/cnn-text-classification-tf).

 ## Performance compared to original paper
 I use the same pretrained word2vec [GoogleNews-vectors-negative300.bin](https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing) in Kim's paper. However, I don't implement L2-normalization of weight on penultimate layer, but provide a L2-normalization of gradients.
 Finally, I got a best dev accuracy 80.1%, close to 81% that reported in the original paper.

 ## Data
 Please download the corpus from this repository [cnn-text-classification-tf](https://github.com/dennybritz/cnn-text-classification-tf), :)

 'data/rt.vec', this file was trained on the corpus by word2vec tool. I recommend to use GoogleNews word2vec, which could get better performance, since
 this corpus is small (contains about 10K sentences).

 When using GoogleNews word2vec, this code loads it with gensim tools [gensim](https://github.com/piskvorky/gensim/tree/develop/gensim/models).

 ## Remark
 If I were wrong in CNN implementation via mxnet, please correct me.

 ## References
 - ["Implementing a CNN for Text Classification in Tensorflow" blog post.](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/)
 - [Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1408.5882)
	Implementing CNN for Text Classification in MXNet
	============
	It is slightly simplified implementation of Kim's [Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1408.5882) paper in MXNet.

	Recently, I have been learning mxnet for Natural Language Processing (NLP). I followed this nice blog ["Implementing a CNN for Text Classification in Tensorflow" blog post.](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/) to reimplement it by mxnet framwork.
	Data preprocessing code and courpus are directly borrowed from original author [cnn-text-classification-tf](https://github.com/dennybritz/cnn-text-classification-tf).

	## Performance compared to original paper
	I use the same pretrained word2vec [GoogleNews-vectors-negative300.bin](https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing) in Kim's paper. However, I don't implement L2-normalization of weight on penultimate layer, but provide a L2-normalization of gradients.
	Finally, I got a best dev accuracy 80.1%, close to 81% that reported in the original paper.

	## Data
	Please download the corpus from this repository [cnn-text-classification-tf](https://github.com/dennybritz/cnn-text-classification-tf), :)

	'data/rt.vec', this file was trained on the corpus by word2vec tool. I recommend to use GoogleNews word2vec, which could get better performance, since
	this corpus is small (contains about 10K sentences).

	When using GoogleNews word2vec, this code loads it with gensim tools [gensim](https://github.com/piskvorky/gensim/tree/develop/gensim/models).

	## Remark
	If I were wrong in CNN implementation via mxnet, please correct me.

	## References
	- ["Implementing a CNN for Text Classification in Tensorflow" blog post.](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/)
	- [Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1408.5882)