Korean morphological analyzer (한국어 형태소 분석기)

PyTorch implementation for Korean morphological analyzer

Dependency

• PyTorch >= 1.1
• torchtext
• Check the requirements.txt

pip install -r requirements.txt

Getting Started

Step 1: Prepare the data

The sample data can be found data/ directory. The data consists of eojoel and pairs of morphmeme and POS tag.

Step 2: Train the model

python train.py 

This will load a config file (config/kma.yaml) and run the model defined by the config file, which consists of a 3-layer LSTM with 100 hidden units on the bidirectional encoder and a Pointer-generator network and a CRF tagger. The detailed parameters can be found config/ directory.

Step 3: Tagging

python tagging.py --input_file text_file --output output_file

We have a model which you can use to tag on new data. It reads sentences line by line and executes the tagging. The tagged outputs are saved into output_file.

Pretrained model

• Pretrained models can be downloaded download

Citation

@inproceedings{song-park-2019-korean,
  title="{K}orean Morphological Analysis with Tied Sequence-to-Sequence Multi-Task Model",
  author="Song, Hyun-Je and Park, Seong-Bae",
  booktitle="Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
  pages="1436--1441",
  year="2019"
}

Acknowledgement

The implementation is highly inspired from IBM's seq2seq and OpenNMT-py.