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Author: SuyashLakhotia

README.md

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+# Text Categorization
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+> This repository contains the source code and other helper files for my undergraduate thesis titled "Graph Convolutional Neural Networks for Text Categorization" under the supervision of [Prof. Xavier Bresson](http://www.ntu.edu.sg/home/xbresson/) at Nanyang Technological University, Singapore.
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+There are a total of three benchmark models and three deep learning models implemented in this repository for text classification:
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+1. `baseline.py`: Linear SVC & Multinomial Naive Bayes
+2. `mlp.py`: Multilayer Perceptron
+3. `cnn_fchollet.py`: F. Chollet CNN (based on this [2016 blog post](https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html))
+4. `cnn_ykim.py`: Y. Kim CNN (based on [Y. Kim, 2014](https://arxiv.org/abs/1408.5882))
+5. `graph_cnn.py`: Graph CNN (based on [M. Defferrard et al., 2017](https://arxiv.org/abs/1606.09375))
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+The above models were tested on three datasets — [Rotten Tomatoes Sentence Polarity Dataset](http://www.cs.cornell.edu/people/pabo/movie-review-data/), [20 Newsgroups](http://qwone.com/~jason/20Newsgroups/) & [RCV1](http://www.jmlr.org/papers/volume5/lewis04a/lewis04a.pdf). The code used to preprocess the datasets can be found [here](data.py) and the performance of the models on these datasets can be found [here](results.csv).