Cutkum ['คัดคำ']

Cutkum ('คัดคำ') is a python code for Thai Word-Segmentation using Recurrent Neural Network (RNN) based on Tensorflow library.

Cutkum is trained on BEST2010, a 5 Millions Thai words corpus by NECTEC (https://www.nectec.or.th/). It also comes with an already trained model, and can be used right out of the box. Cutkum is still a work-in-progress project. Evaluated on the 10% hold-out data from BEST2010 corpus (~600,000 words), the included trained model currently performs at

98.0% recall, 96.3% precision, 97.1% F-measure (character-level) RC: 0.988, PC: 0.966, FC: 0.977 95% recall, 95% precision and 95.0% F-measure (word-level -- same evaluation method as BEST2010)

Update :D

A major update

1. now you dont have to load the model seperately, just do pip install and Cutkum is ready to use out of the box.
2. the included model is now smaller, faster, and have higher accuracy. :)

Requirements

• python = 2.7, 3.0+
• tensorflow = 1.4+

Installation

cutkum can be installed using pip

pip install cutkum

Usages

Once installed, you can use cutkum within your python code to tokenize thai sentences.

>>> from cutkum.tokenizer import Cutkum

>>> ck = Cutkum()
>>> words = ck.tokenize("สารานุกรมไทยสำหรับเยาวชนฯ")

# python 3.0
>>> words
['สารานุกรม', 'ไทย', 'สำหรับ', 'เยาวชน', 'ฯ']

# python 2.7
>>> print("|".join(words)) 
# สารานุกรม|ไทย|สำหรับ|เยาวชน|ฯ

You can also use cutkum straight from the command line.

usage: cutkum [-h] [-v]
              (-s SENTENCE | -i INPUT_FILE | -id INPUT_DIR)
              [-o OUTPUT_FILE | -od OUTPUT_DIR] [--max | --viterbi]

cutkum -s "ล่าสุดกระทรวงพาณิชย์ได้ประกาศตัวเลขการส่งออกของไทย"

# output as
ล่าสุด|กระทรวงพาณิชย์|ได้|ประกาศ|ตัว|เลข|การ|ส่ง|ออก|ของ|ไทย

cutkum can also be used to segment text within a file (with -i), or to segment all the files within a given directory (with -id).

cutkum -i input.txt -o output.txt
cutkum -id input_dir -od output_dir

Citation

Pucktada Treeratpituk (2017). Cutkum: Thai Word-Segmentation with LSTM in Tensorflow. May 5, 2017. See https://github.com/pucktada/cutkum

License

This project is licensed under the MIT License - see the LICENSE file for details

To Do

• Improve performance, with better better model, and better included trained-model
• Improve the speed when processing big file