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https://github.com/Cartus/AMR-Parser
Better Transition-Based AMR Parsing with a Refined Search Space (authors' DyNet implementation for the EMNLP18 paper)
https://github.com/Cartus/AMR-Parser
aligner amr-parser nlp semantic-parsing transition-based-parser
Last synced: about 2 months ago
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Better Transition-Based AMR Parsing with a Refined Search Space (authors' DyNet implementation for the EMNLP18 paper)
- Host: GitHub
- URL: https://github.com/Cartus/AMR-Parser
- Owner: Cartus
- License: mit
- Created: 2018-11-18T04:49:57.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-06-13T13:38:13.000Z (over 5 years ago)
- Last Synced: 2024-08-03T12:15:50.098Z (5 months ago)
- Topics: aligner, amr-parser, nlp, semantic-parsing, transition-based-parser
- Language: Python
- Homepage: https://aclweb.org/anthology/papers/D/D18/D18-1198/
- Size: 6.4 MB
- Stars: 10
- Watchers: 2
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Better Transition-Based AMR Parsing with a Refined Search Space
[DyNet](https://github.com/clab/dynet) implementation of the transition-based AMR parser. We provide the code for aligner and the parser. You can find all the details in the following paper:
- [ Better Transition-Based AMR Parsing with a Refined Search Space](http://www.statnlp.org/research/sp/zhijiang18emnlp.pdf). (Zhijiang Guo and Wei Lu. EMNLP 2018)
See below for an example for this transition-based AMR parser:
## Requirements
In order to train the parser, you will need to have the following installed:
- Python 3.5.2
- [DyNet 2.0](https://github.com/clab/dynet)
- [JAMR](https://github.com/jflanigan/jamr)
- [MGIZA++](https://github.com/moses-smt/mgiza)
- [NLTK](https://www.nltk.org/)
Notice that we do not support DyNet version higher than 2.0.
### MGIZA++ Installation (Ubuntu)
- Preparation:
```
sudo apt-get install cmake
sudo apt-get install libboost-all-dev
```
- Installing: clone the AMR-Parser first, then put the MGIZA++ inside the data folder
```
cd data
git clone https://github.com/moses-smt/mgiza.git
cd mgiza/mgizapp
cmake .
make
make install
```
### NLTK Tagger Installation
- Installing (under the python3 environment):
```
import nltk
nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet')
```
## Usage
### Hybrid Aligner
Get the AMR corpus (LDC2014T12/LDC2015E86/LDC2017T10). We can't distribute them because them paid. Put the train/dev/test folder of the corresponding AMR corpus (under the split folder) to the folder /data/amr/data/amrs/split. Then we run the preprocess script.
- For LDC2014T12 corpus, run the bash script:
```
./preprocess_14.sh
```
- For LDC2015E86, run the bash script:
```
./preprocess_15.sh
```
- For LDC2017T10 corpus, run the bash script:
```
./preprocess_17.sh
```
Aftering running the script, you can find preprocessed AMR files under the amr/tmp_amr folder, which contains train, dev and test three folders. Before using our Hybrid Aligner, we need the output files of [JAMR Aligner](https://github.com/jflanigan/jamr).
You can get the aligned train file simply by using the JAMR aligner:
```
. scripts/config.sh
scripts/ALIGN.sh < amr.txt > train.txt
```
Then we put three aligned files into one folder called jamr_ouput inside the data folder. Then you can run the bash script of the Hybrid Aligner to get the transition sequences for training the AMR parser:
```
./align.sh
```
Finally, we get four new files: train.transtions, dev.transitions, test.transitions and train.txt.pb.lemmas. train.txt.pb.lemmas is the look-up table for predicate and lemma concept (look at the paper for more details.)
### AMR Parser
Now we are ready for train our own AMR parser.
#### Train a new AMR parser
```
python3 train.py
```
Link to the word vectors that we used in the ACL 2015 paper for English: [sskip.100.vectors](https://drive.google.com/file/d/0B8nESzOdPhLsdWF2S1Ayb1RkTXc/view?usp=sharing). The training process should be stopped when the development SMATCH score does not substantially improve anymore (around epoch 14-16).
#### Parsing with a trained parser
If you want to use a pretrained parser, just put the pretrain model under the result folder. Then run the script (here we use the pretrained model for the AMR2014 corpus as an example):
```
python3 train.py --load_model 1 --model result/pretrain14.model --dev_file data/test.transitions --gold_AMR_dev data/amr/tmp_amr/test/amr.txt
```
Pretrained models can be downloaded:
- [pretrain14.model](https://drive.google.com/open?id=1yZZfb7ba4o8Xx5bjozbUXABKszvVlrOd): Parser trained on AMR2014 corpus.
- [pretrain15.model](https://drive.google.com/open?id=15B94w28AWsFE3Wx9ZfiuNxbK7phqP6O_): Parser trained on AMR2015 corpus.
### Related Repo
The code of the unsupervised aligning is adapted from this [repository](https://github.com/melanietosik/string-to-amr-alignment).
### Citation
If you make use of this software, please cite the following:
@InProceedings{D18-1198,
author = "Guo, Zhijiang and Lu, Wei",
title = "Better Transition-Based AMR Parsing with a Refined Search Space",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
year = "2018"
}