https://github.com/richliao/textClassifier
Text classifier for Hierarchical Attention Networks for Document Classification
https://github.com/richliao/textClassifier
attention-mechanism convolutional-neural-networks hierarchical-attention-networks recurrent-neural-networks text-classification
Last synced: 6 months ago
JSON representation
Text classifier for Hierarchical Attention Networks for Document Classification
- Host: GitHub
- URL: https://github.com/richliao/textClassifier
- Owner: richliao
- License: apache-2.0
- Created: 2016-12-29T03:02:44.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2021-09-16T14:35:50.000Z (about 4 years ago)
- Last Synced: 2024-11-06T02:38:46.281Z (11 months ago)
- Topics: attention-mechanism, convolutional-neural-networks, hierarchical-attention-networks, recurrent-neural-networks, text-classification
- Language: Python
- Homepage:
- Size: 21.5 KB
- Stars: 1,069
- Watchers: 43
- Forks: 378
- Open Issues: 30
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# textClassifier
textClassifierHATT.py has the implementation of [Hierarchical Attention Networks for Document Classification](https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf). Please see the [my blog](https://richliao.github.io/supervised/classification/2016/12/26/textclassifier-HATN/) for full detail. Also see [Keras Google group discussion](https://groups.google.com/forum/#!topic/keras-users/IWK9opMFavQ)
textClassifierConv has implemented [Convolutional Neural Networks for Sentence Classification - Yoo Kim](https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf). Please see the [my blog](https://richliao.github.io/supervised/classification/2016/11/26/textclassifier-convolutional/) for full detail.
textClassifierRNN has implemented bidirectional LSTM and one level attentional RNN. Please see the [my blog](https://richliao.github.io/supervised/classification/2016/12/26/textclassifier-RNN/) for full detail.
## update on 6/22/2017 ##
To derive the attention weight which can be useful to identify important words for the classification. Please see my latest update on the post. All you need to do is run a forward pass right before attention layer output. The result is not very promising. I will update the post once I have further result.
---
This repo is forked from [https://github.com/richliao/textClassifier](https://github.com/richliao/textClassifier) and we find some issue [here](https://github.com/richliao/textClassifier/issues/28). So we update the textClassifierHATT with `python 2.7` and `keras 2.0.8`
```
# clone the repo
git clone {repo address}
# install Dependent library
cd textClassifier
pip install -r req.xt
# download imdb train from Kaggle in the below link and keep the files in the working directory
https://www.kaggle.com/c/word2vec-nlp-tutorial/download/labeledTrainData.tsv
# download glove word vector
wget http://nlp.stanford.edu/data/glove.6B.zip
unzip glove.6B.zip
# install nltk 'punkt' using the following code in python interpretor
>>>import nltk
>>>nltk.download('punkt')
# train the model
python textClassifierHATT.py
# note if in case while installing word2vec, cython error occurs then
pip install --upgrade cython
```
Enjoy！