Presented here is a method to modify the word embeddings of a word in a sentence with its surrounding context using a bidirectional Recurrent Neural Network (RNN). The hypothesis is that these modified embeddings are a better input for performing text classification tasks like sentiment analysis or polarity detection.

Read the full blog post here: chaitjo.github.io/context-embeddings

The code implements the proposed model as a pre-processing layer before feeding it into a Convolutional Neural Network for Sentence Classification (Kim, 2014). Two implementations are provided to run experiments: one with tensorflow and one with tflearn (A high-level API for tensorflow). Training happens end-to-end in a supervised manner: the RNN layer is simply inserted as part of the existing model's architecture for text classification.

The tensorflow version is built on top of Denny Britz's implementation of Kim's CNN, and also allows loading pre-trained word2vec embeddings.

Although both versions work exactly as intended, results in the blog post are from experiments with the tflearn version only.

Tensorflow code is divided into model.py which abstracts the model as a class, and train.py which is used to train the model. It can be executed by running the train.py script (with optional flags to set hyperparameters)-

$ python train.py [--flag=1]

(Tensorflow code for Kim's baseline CNN can be found in /cnn-model.)

Tflearn code can be found in the /tflearn folder and can be run directly to start training (with optional flags to set hyperparameters)-

$ python tflearn/model.py [--flag=1]

The summaries generated during training (saved in /runs by default) can be used to visualize results using tensorboard with the following command-

$ tensorboard --logdir=<path_to_summary>