A Thai word tokenization library using Deep Neural Network.
Faster tokenization by code refactorization from our new contributor: Titipat Achakulvisut
The Convolutional Neural network is trained from 90% of NECTEC's BEST corpus(consists of 4 sections, article, news, novel and encyclopedia) and test on the rest 10%. It is a binary classification model trying to predict whether a character is the beginning of word or not. The results calculated from only 'true' class are as follow
- f1 score: 98.8%
- precision score: 98.6%
- recall score: 99.1%
Install using pip
for stable release,
pip install deepcut
For latest development release,
pip install git+git://github.com/rkcosmos/deepcut.git
Or clone the repository and install using setup.py
python setup.py install
Make sure you are using tensorflow
backend in Keras
by making sure ~/.keras/keras.json
is as follows (see also https://keras.io/backend/)
{
"floatx": "float32",
"epsilon": 1e-07,
"backend": "tensorflow",
"image_data_format": "channels_last"
}
We do not add tensorflow
in automatic installation process because it has cpu and gpu version. Installing cpu version to everyone might break those who already have gpu version installed. So please install tensorflow yourself following this guildline https://www.tensorflow.org/install/.
import deepcut
deepcut.tokenize('ตัดคำได้ดีมาก')
Output will be in list format
['ตัด','คำ','ได้','ดี','มาก']
Some texts might not be segmented as we would expected (e.g. 'โรงเรียน' -> ['โรง', 'เรียน']), this is because of
-
BEST corpus (training data) tokenizes word this way (They use 'Compound words' as a criteria for segmentation)
-
They are unseen/new words -> Ideally, this would be cured by having better corpus but it's not very practical so I am thinking of doing semi-supervised learning to incorporate new examples.
Any suggestion and comment are welcome, please post it in issue section.
- Rakpong Kittinaradorn
- Korakot Chaovavanich
- Titipat Achakulvisut
And we are open for contribution and collaboration.