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https://github.com/ch4osmy7h/framenetparser
PyTorch code for "A Graph-Based Neural Model for End-to-End Frame Semantic Parsing" (EMNLP2021)
https://github.com/ch4osmy7h/framenetparser
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PyTorch code for "A Graph-Based Neural Model for End-to-End Frame Semantic Parsing" (EMNLP2021)
- Host: GitHub
- URL: https://github.com/ch4osmy7h/framenetparser
- Owner: Ch4osMy7h
- Created: 2021-08-31T03:21:41.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-08-12T07:09:38.000Z (over 2 years ago)
- Last Synced: 2024-06-29T08:26:23.624Z (7 months ago)
- Language: Python
- Size: 51.8 KB
- Stars: 16
- Watchers: 4
- Forks: 5
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
# A Graph-Based Neural Model for End-to-End Frame Semantic Parsing
This repository is the pytorch implementation of our paper:
**A Graph-Based Neural Model for End-to-End Frame Semantic Parsing**
Zhichao Lin, Yueheng Sun, [Meishan Zhang](http://zhangmeishan.github.io//)
The Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021
**!!!Note: we have upgrade our codebase to allennlp 2.0+ which is different from our origin implementation based on alennnlp 1.0. We find that our model can achieve further performance about 49.20 ROLE-F1 in Framenet1.5 and 49.05 ROLE-F1 in Framenet 1.7.**
## Installation
Clone the current repository:
```bash
git clone https://github.com/Ch4osMy7h/FramenetParser.git
cd FramenetParser
```
### Allennlp and Other Dependencies
```bash
# Create python environment (optional)
conda create -n framenet python=3.7
source activate framenet
# Install python dependencies
pip install -r requirements.txt
```
### Data
Request and download Framenet Dataset 1.5 and Framenet Dataset 1.7 from [Website]("https://framenet.icsi.berkeley.edu/fndrupal/"). Place them under `data` directory, which contatins two subdirectories called `fndata-1.5` and `fndata-1.7`. Specially, the data should be splited into there subdirectories following [FN1.5](https://github.com/swabhs/scaffolding/blob/master/allennlp/data/dataset_readers/framenet/full_text_reader.py) and [FN1.7](https://github.com/swabhs/open-sesame/blob/625da3d451/sesame/globalconfig.py). Structures are showed below:
```bash
./data
fndata-1.5/
train/ # containing the training xml files
dev/
test/
...
fndata-1.7/
train/
dev/
test/
...
```
To get the datasets our code based on, run the following commands:
```bash
python preprocess.py
```
## Training & Evaluation
We use train_parser.sh script to train and evaluate all of our joint models. You can modify any configuration you want in the script.
```bash
bash train_parser.sh
```
### Evaluation Only
All models can be evaluated using by the following commands:
```bash
allennlp evaluate your_evaluation_model \
your_evaluation_data_file \
--include-package framenet_parser \
--output-file your_evaluation_output.json \
```
## Inference on unlabled data
For predicting targets, frames and frame element on your own unlabed data (follow the file format as shown in `experiments/inference/sample.json`), you can use the following commands:
```bash
allennlp predict \
--output-file your_wanted_output_file \
--include-package framenet_parser \
--predictor framenet_parser \
--cuda-device 0 \
your_trained_model \
your_own_data.json
```
## TODOs:
- [ ] An easy-to-use pipeline models for specific usages (will be published recently)
- [x] An easy-to-use full joint model for extracting frame-semantic structures