Code of Text Classification with Topic-based Word Embedding and Convolutional Neural Networks (ACM BCB 2016).
In this paper, we propose a novel neural language model, Topic-based Skip-gram, to learn topic-based word embeddings for text classification with CNNs.
Please cite our paper if it helps your research:
@inproceedings{xu2016text,
title={Text Classification with Topic-based Word Embedding and Convolutional Neural Networks.},
author={Xu, Haotian and Dong, Ming and Zhu, Dongxiao and Kotov, Alexander and Carcone, April Idalski and Naar-King, Sylvie},
booktitle={The 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB)},
pages={88--97},
year={2016}
}- Python==2.7
- Theano==0.7
- Lasagne==0.1
- Pre-trained word2vec vectors (CNN-gn in the paper) and Skip-gram model are available here.
- Latent Dirichlet Allocation model can be downloaded here.
To process raw textual data,
python process_data_channel.py /path/to/wordvec1 /path/to/wordvec2 LengthofWordvec1 LengthofWordvec2 classnum /path/to/data_class0 /path/to/data_class1
OR
python process_data_multi.py /path/to/wordvec1 /path/to/wordvec2 LengthofWordvec1 LengthofWordvec2 classnum /path/to/data_class0 /path/to/data_class1
python conv_channel_w2v.py epochnum batchsize
OR
python conv_multi_w2v.py epochnum batchsize
You can choose epochnum=25 and batchsize=64.
The preprocessing code is adapted from Dr. Kim's work.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent