CNN forText Classification describes how convolutional neural networks (CNNs) can be used for text classification. CNNs apply convolution operations to input data to identify patterns. For natural language processing, CNNs convolve filters over word vectors or embeddings to analyze n-grams. The model is adapted from a 2014 paper and applies convolution and max pooling layers to classify sentences.

1. CNN forText
Classification
Tim Lee andWill Kelly

2. What are
Convolutions?
A convolution is an
integral that expresses the
amount of overlap of one
function as it is shifted
over another function
Can be thought as
"blending" functions
Pictures found on Christopher Olah's blog, originally
from Wikipedia 2
Definition from Wolfram Alpha's page on
convolution 1

3. How does this
work?
ComputerVision NLP
Word2Vec
GloVe
One-hot
Images via WildML
3

4. Convolution
Layers
Picture found on Christopher Olah's blog 4

5. More
complexity &
Max-pooling
Picture
found on
Christopher
Olah's blog
4
Image via
WildML 3

6. The Model
Adapted from KimYoon's 2014 paper, Convolutional
Neural Networks for Sentence Classification 5

7. References
 1. http://mathworld.wolfram.com/Convolution.html
 2.
https://en.wikipedia.org/wiki/Convolution#/media/File:Convolutio
n_of_box_signal_with_itself2.gif
 3. http://www.wildml.com/2015/11/understanding-convolutional-
neural-networks-for-nlp/
 4. http://colah.github.io/posts/2014-07-Conv-Nets-Modular/
 5. http://www.wildml.com/2015/12/implementing-a-cnn-for-text-
classification-in-tensorflow/