relation_extraction's Introduction

Relation_Extraction

Relation Classification via Convolutional Deep Neural Network

The code is an implementation of the paper http://www.aclweb.org/anthology/C14-1220 using tensorflow.

##Algorithm

I almost followed the technique used in the paper mentioned above, only tweaking with some parameters such as dimensions of word vector, position vectors, optimization function and so on.
Basic architecture is a convolution layer, max pool and final softamx layer. We can always add/delete the number of conv and max-pool layers b/w the input layer and the final softmax layer. I used only 1 conv and 1 max pool.

##Files

text_cnn.py - It is a class which implements the architecture of the model. So it accepts the input, contains all the layers such as conv2d(convolution layer), max_pool etc. which process the input vector and finally gives the output in terms of predictions for each class.
data_helpers.py - It is a generic script which contains helpers such as generating batches, loading the training data etc.
train.py - This module creates the input vector from the training data, and finally trains the model on the data and saves it on the disk.
temp.py - This is a pyspark code used to fetch data from the HBase table and predict the class of each row using the trained model.

##Challenges

My training data is around 7K rows. Due to this, the accuracy is around 70.34% on the test set. So as the training set grows, I'm sure the model will perform much better.
My data set consists of inter sentencial entities with entities linked with a cause-effect relationship. However, this model can extended to a n class problem.

##TODO