https://github.com/chaitjo/lstm-context-embeddings

Augmenting word embeddings with their surrounding context using bidirectional RNN
https://github.com/chaitjo/lstm-context-embeddings

cnn context-embeddings deep-learning lstm neural-networks sentence-classification tensorflow word-embeddings

Last synced: 13 days ago
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Augmenting word embeddings with their surrounding context using bidirectional RNN

• Host: GitHub
• URL: https://github.com/chaitjo/lstm-context-embeddings
• Owner: chaitjo
• License: mit
• Created: 2016-07-21T07:53:45.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2020-02-18T12:05:56.000Z (over 4 years ago)
• Last Synced: 2023-03-05T10:08:17.563Z (over 1 year ago)
• Topics: cnn, context-embeddings, deep-learning, lstm, neural-networks, sentence-classification, tensorflow, word-embeddings
• Language: Python
• Homepage: https://chaitjo.github.io/context-embeddings/
• Size: 23.2 MB
• Stars: 59
• Watchers: 3
• Forks: 20
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Overview

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](https://chaitjo.github.io/context-embeddings/)**

---

![Bidirectional RNN layer](res/bidirectional-rnn.png)

# Implementation

The code implements the proposed model as a pre-processing layer before feeding it into a [Convolutional Neural Network for Sentence Classification](https://arxiv.org/pdf/1408.5882v2.pdf) (Kim, 2014). Two implementations are provided to run experiments: one with [tensorflow](https://www.tensorflow.org/) and one with [tflearn](http://tflearn.org/) (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](https://github.com/dennybritz/cnn-text-classification-tf), 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.

# Usage

I used Python 3.6 and Tensorflow 0.12.1 for my experiments.

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=

```