parserChiang - Nave Transition-based Dependency Parser in Gluon

This repo support CoNLL format, which is adapted by Universal Dependencies Project. parserChiang is implemented with great MXNet gluon.

Models

There are different models in this repo: 1. [DEPRECATED] default/: The default parser model using only word features. It is the baseline of all other models. 2. [DEPRECATED] pos_aid/: This parser model requires standard POS tagging during inference, which is provided in CoNLL dataset. In practice, you may use Stanford NLP tools to get good POS tags. 3. [DEPRECATED] pos_joint/: This parser model will predict POS tags. 4. pos_deprel_joint/: This parser model will predict POS tags and dependent relation label. LAS index requires the output from this model. 5. pos_aid_deprel_joint/: This parser model requires standard POS tagging during inference, and will predict ependent relation label.

The models marked with [DEPRECATED] will not be updated to latest functions.

Usage

Data should be put into data/ directory. Train the model with

$ python3 train_parser.py

Then it will create a directory named model_dumps_{Date}_{Time} to store the model dump. Test it with

$ python3 test_parser.py [model_path] [model_file]

Notes

This implementation is a low-performance transition-based parser in both training speed and predicition accuracy. I created it as a toy model simply for learning natural language processing. DO NOT USE IT IN ANY REAL WORLD TASKS.

Have fun with it!

License

Copyright 2017 Mengxiao Lin