Text-Classification-of-the-shelf-with-a-normalizer-for-Arabic-text-

This is a simple text classification library based on keras associated with a text normalization script for Arabic

Current Implemented Models:

1- Word Level CNN based on: "Convultion Neural Network for Text Classificartion" url: http://www.aclweb.org/anthology/D14-1181

2- Word Level C-LSTM based on: "A C-LSTM Neural Network for Text Classification" url:https://arxiv.org/pdf/1511.08630.pdf

3- Recurrent Network and its variants (BiLSTM, LSTM, GRU, BiGRU, Attention-BiLSTM)

4- Models implemented but currently not supported in options (Attention-LSTM,Attention-BiGRU).

5- Not yet tested (char level CNN).

Requirements

keras (2.0 or above)
gensim
numpy
pandas

General Usage:

- Tested with python 3.4 *
python test_baselines.py --train training_file --Ar='True' --dev Dev_File --test test_file --model_type=model_selection --static=Trainable_embeddings --rand=Random_Embeddings --embedding=External_Embedding_model --model_file=Output_model_file_inJson
put your training labels in [link:] https://github.com/EngSalem/Text-Classification-of-the-shelf-with-a-normalizer-for-Arabic-text-/blob/master/conf/label_list

Options details

train: training file assuming in csv format, text, label
Ar: if True then Arabic normalization is applied (should be true in case of external embeddings)
dev: Development file in csv format
test: test file in csv format
model_type: currently support those type of models: (cnn: word level cnn, clstm: word level clstm, lstm: vanilla lstm architecture, blstm: Vanilla bidirectional LSTM, bigru: Vanilla BiGated Recurrent unit, attbilstm: BiLSTM with self attention mechanism)
static: used in case of external embedding, if True: External Embeddings are not fine tuned during training, if False: External EMbeddings are fine tuned during training).
rand: if True, No external embedding is applied, randomly initialized embedding
embedding: External embedding model in gensim format
model_file: Output model file in Json.

Note: final model score is dumped into a file with name_of_model_score with both dev and test scores

Example Project (Arabic Dialect Identification with Deep Models)

This project utilize 6 deep learning models applied on Arabic Online Commentary Dataset
url: https://www.cs.jhu.edu/~ccb/publications/arabic-dialect-corpus.pdf
dataset url: https://www.cis.upenn.edu/~ccb/data/AOC-dialectal-annotations.zip
make sure to cite AOC oringial paper if you are going to use it in your work.
This work currently accepted to VarDial Worshop 2018 co-located with COLING 2018 under the name (paper soon) "Deep Models for Arabic Dialect Identification on Benchmarked Data"
Training data link: [link]: https://github.com/EngSalem/Text-Classification-of-the-shelf-with-a-normalizer-for-Arabic-text-/tree/master/data/train
Dev data link: [link]: https://github.com/EngSalem/Text-Classification-of-the-shelf-with-a-normalizer-for-Arabic-text-/tree/master/data/dev
Test data link: [link]: https://github.com/EngSalem/Text-Classification-of-the-shelf-with-a-normalizer-for-Arabic-text-/tree/master/data/test
An example on how to use it is in: https://github.com/EngSalem/Text-Classification-of-the-shelf-with-a-normalizer-for-Arabic-text-/blob/master/run.sh
If you are going to follow up on this project please cite this work using the following bibtext:

@inproceedings{Elaraby2018,
  title={Deep Models for Arabic Dialect Identification on Benchmarked Data},
  author={Elaraby, Mohamed and Abdul-Mageed, Muhammad},
  booktitle={Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial5)},
  year={2018}
}

External Embedding Models

For Arabic Dialects, we release 2 embedding models
AOC embedding: Download in url: [link] https://drive.google.com/open?id=1QEg9HotnTCI45-PT52g445bp5qYQ4RSm
Twitter Embedding Model: Download in url: [link] https://drive.google.com/open?id=1hEuNHn2PA7kIf1IK0FUGUskA77YZJ3vO
The embedding models are released exclusively for research purposes.
Please cite the paper if you use our embedding models.

@inproceedings{abdul2018you,
  title={You tweet what you speak: A city-level dataset of arabic dialects},
  author={Abdul-Mageed, Muhammad and Alhuzali, Hassan and Elaraby, Mohamed},
  year={2018},
  organization={LREC}
}

hasanhuz / ArabicTextClassification