A TensorFlow implementation of Graph-based Biaffine Parser
Table of Contents
- Requirements
- GloVe
- Data Format
- Train a Supertagger
- Structure of the Code
- Jackknife POS Tagging
- Run a pre-trained TAG Parser
- Run demo
- Notes
Requirements
TensorFlow needs to be installed before running the training script. TensorFlow 1.0.0 or higher is supported. (TensorFlow 1.3.0 and 1.7.0 are tested)
GloVe
Our architecture utilizes pre-trained word embedding vectors, GloveVectors. Run the following:
wget http://nlp.stanford.edu/data/glove.6B.zip and save it to a sub-directory glovevector/.
Data Format
The biaffine parser takes as input a file in the Conllu+Supertag (conllustag) format, in which one column for supertags is added to the original conllu format at the end. See a sample.
Train a Parser
All you need to do is to create a new directory for your data in the conllustag format and a json file for the model configuration and data information. We provide a sample json file for the sample data directory. You can train a parser on the sample data by the following command:
python train_graph_parser.py sample_data/config_demo.jsonAfter running this command, you should be getting the following files and directories in sample_data/:
| Directory/File | Description |
|---|---|
| checkpoint.txt | Contains information about the best model. |
| sents/ | Contains the words in the one-sentence-per-line format |
| gold_pos/ | Contains the gold POS tags in the one-sentence-per-line format |
| gold_stag/ | Contains the gold supertags in the one-sentence-per-line format |
| arcs/ | Contains the gold arcs in the one-sentence-per-line format |
| rels/ | Contains the gold rels in the one-sentence-per-line format |
| predicted_arcs/ | Contains the predicted arcs in the one-sentence-per-line format |
| predicted_rels/ | Contains the gold rels in the one-sentence-per-line format |
| Parsing_Models/ | Stores the best model. |
| conllu/sample.conllustag_stag | Contains the predicted supertags in the conllustag format |
Structure of the Code
| File | Description |
|---|---|
utils/preprocessing.py |
Contains tools for preprocessing. Mainly for tokenizing and indexing words/tags. Gets imported to utils/data_process_secsplit.py |
utils/data_process_secsplit.py |
Reads training and test data and tokenize/index words, POS tags, stags, and characters. |
utils/parsing_model.py |
Contains the Parsing_Model class that constructs our LSTM computation graph. The class has the necessary methods for training and testing. Gets imported to bilstm_stagger_model.py. For more details, read README for utils. |
utils/lstm.py |
Contains tensorflow LSTM equations. Gets imported to utils/stagging_model.py. |
graph_parser_model.py |
Contains functions that instantiate the Parsing_Model class and train/test a model. Gets imported to graph_parser_main.py |
graph_parser_main.py |
Main file to run experiments. Reads model and data options. |
scripts/train_graph_parser.py |
Runs graph_parser_main.py in bash according to the json file that gets passed. |
Run a pre-trained TAG Parser
We provide a pre-trained TAG parser.
To run the pretrained parser on your data, first download the model and place the Pretrained_Parser directory in the demo directory. Then, run
- With tokenization
python demo/scripts/demo_model.py --infile demo/sents/test.txt --tokenize- Without tokenization
python demo/scripts/demo_model.py --infile demo/sents/test.tokenized.txtYou can replace these files by your own data. It prints out predicted elementary trees, parses, and PTB-style fine-grained POS tags in the conllu format. Note that we put elementary trees in the UPOS column.
Run a demo TAG Parser
Enjoy playing around with our TAG demo parser online!
Notes
If you use this tool for your research, please consider citing:
@InProceedings{Kasai&al.18,
author = {Jungo Kasai and Robert Frank and Pauli Xu and William Merrill and Owen Rambow},
title = {End-to-end Graph-based TAG Parsing with Neural Networks},
year = {2018},
booktitle = {Proceedings of NAACL},
publisher = {Association for Computational Linguistics},
}