A Jupyter notebook that implements an approach to generating embeddings for Unicode emoji from their descriptions, based on the work by Ben Eisner and his colleagues at the University College London Machine Reading Group [1].

• Python 3.5.2
• jupyter 4.2.0

• numpy 1.11.1
• pandas 0.18.1
• matplotlib 1.5.3
• Keras 1.1.2
• scikit-learn 0.17.1
• h5py 2.6.0
• hdf5 1.8.17

Although the approach taken to generating embeddings is based on that taken in the paper, it is different in a number of respects.

• We use GloVe [2] as the source of pre-defined word embeddings. Specifically, the 300-dimensional version of the Wikipedia 2014 and Gigaword 5 embeddings (provided as glove.6b.300d.txt) , as opposed to the 300-dimensional Google News word2vec embeddings used in [1].
• Following a suggestion by Eisner [3], we use a bidirectional GRU to compute a 300-dimensional embedding for the emoji description, as opposed to the paper's straightforward summation of the embeddings of the constituent terms.
• Instead of taking the sigmoid of the dot product of the emoji and description embeddings, we concatenate the embeddings into a 600-dimensional vector and then pass that up through several densely-connected layers, ending with a 2-dimensional softmax output layer. The downside of this is the additional time taken in training relative to that reported for the implementation in [1].

The Keras model architecture is shown below:

The model uses categorical cross-entropy as a loss function and uses Adam for optimization. We run training for 80 epochs and then save the weights from the emoji embedding layer of the model checkpoint with the maximum categorical accuracy. Training takes approximately 20 minutes, using Tensorflow as a backend for Keras on an Amazon Web Services EC2 p2-xlarge GPU compute instance.

This architecture takes a good deal of inspiration from the emerging architectural patterns in deep learning for natural language inference, e.g. as described in [4] and [5]. We hope to exploit these architectural patterns to create embeddings for terms from controlled vocabularies, where descriptions will be longer than those associated with Unicode emoji.

Interestingly, in spite of the differences described above, a t-SNE visualization of the computed embeddings shows clusterings recognizable from those in the visualization published in [1]:

Simply run the notebook using the standard Jupyter command:

$ jupyter notebook

Apart from running the notebook, one can view a t-SNE visualization of the computed embeddings by running the following command:

$ python visualize.py

MIT. See the LICENSE file for the copyright notice.

[1] Ben Eisner, Tim Rocktäschel, Isabelle Augenstein, Matko Bošnjak, and Sebastian Riedel. “emoji2vec: Learning Emoji Representations from their Description,” in Proceedings of the 4th International Workshop on Natural Language Processing for Social Media at EMNLP 2016 (SocialNLP at EMNLP 2016), November 2016.

[2] Jeffrey Pennington, Richard Socher, and Christopher D. Manning. "GloVe: Global Vectors for Word Representation," in Proceedings of the 2014 Conference on Empirical Methods In Natural Language Processing (EMNLP 2014), October 2014.

[3] Ben Eisner. Private communication, 22 November 2016.

[4] Anirban Laha and Vikas Raykar. "An Empirical Evaluation of various Deep Learning Architectures for Bi-Sequence Classification Tasks," in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, p. 2762–2773, Osaka, Japan, 11-17 December 2016.

[5] Matthew Honnibal. "Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models", 10 November 2016. Retrieved at https://explosion.ai/blog/deep-learning-formula-nlp on 1 December 2016.