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)
A major update
- now you dont have to load the model seperately, just do
pip installand Cutkum is ready to use out of the box.
- the included model is now smaller, faster, and have higher accuracy. :)
- python = 2.7, 3.0+
- tensorflow = 1.4+
cutkum can be installed using
pip install cutkum
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
Pucktada Treeratpituk (2017). Cutkum: Thai Word-Segmentation with LSTM in Tensorflow. May 5, 2017. See https://github.com/pucktada/cutkum
This project is licensed under the MIT License - see the LICENSE file for details
- Improve performance, with better better model, and better included trained-model
- Improve the speed when processing big file