CapsNet for NLP
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README.md

CapsNet for Natural Language Processing

A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part.

This repository shows how to use a CapsNet architecture for Natural Language Prcoessing tasks like sentiment analysis.

Capsules are introduced by Geoffrey Hinton. We use a CapsNet implementation from 苏剑林 as git submodule. The implementation can be found here.

Related work

Here are some papers where capsules and the CapsNet architecture are introduced:

Paper Authors Link
Dynamic Routing Between Capsules Sara Sabour, Nicholas Frosst, Geoffrey E Hinton here
Matrix capsules with EM routing Geoffrey E Hinton et al. here
Transforming Auto-encoders Geoffrey E. HintonAlex Krizhevsky, Sida D. Wang here

Submodules

The CapsNet implementation is included via git submodule. So the first step after cloning this repository is to initialize the git submodules. This can be done via:

git submodule update --init --recursive

IMDB

We use the IMDB dataset for sentiment analysis with CapsNet. We use a bidirectional GRU before the capsnet layer.

The training can be started with:

python3 main.py

It takes several minutes per epoch. It is highly recommended to use a GPU for training. All experiments are done with a GTX 1060 (6GB).

Results

The following experiments are done on IMDB dataset:

  • Model a): we use a bidirectional GRU with a hidden size of 256. Number of capsule is set to 10. Number of routings is set to 3.
Model Best accuracy
a 88,98 %

Requirements

A recent version of Keras, TensorFlow and h5py is needed. Only Python 3.x is currently supported.

Contact (Bugs, Feedback, Contribution and more)

For questions about the capsnet-nlp repository, contact the current maintainer: Stefan Schweter stefan@schweter.it. If you want to contribute to the project please refer to the Contributing guide!

License

To respect the Free Software Movement and the enormous work of Dr. Richard Stallman the software in this repository is released under the GNU Affero General Public License in version 3. More information can be found here and in COPYING.