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Spico197 / DocEE

🕹️ A toolkit for document-level event extraction, containing some SOTA model implementations.
https://doc-ee.readthedocs.io/
MIT License
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event-extraction information-extraction natural-language-understanding pytorch

readme

❤️ A Toolkit for Document-level Event Extraction with & without Triggers

Build codecov Documentation Status

Hi, there 👋. Thanks for your stay in this repo. This project aims at building a universal toolkit for extracting events automatically from documents 📄 (long texts).

The details can be found in our paper: Tong Zhu, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Nicholas Yuan, Min Zhang. Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Main Track (IJCAI'22). Pages 4552-4558.

🔥 We have an online demo [here] (available in 9:00-17:00 UTC+8).

Currently, this repo contains PTPCG, Doc2EDAG and GIT models, and these models are all designed for document-level event extraction without triggers. Here are some basic descriptions to help you understand the characteristics of each model:

⚙️Installation

Make sure you have the following dependencies installed.

# don't forget to install the dee package
$ git clone https://github.com/Spico197/DocEE.git
$ pip install -e .
# or install directly from git
$ pip install git+https://github.com/Spico197/DocEE.git

🚀Quick Start

💾Data Preprocessing

# ChFinAnn
## You can download Data.zip from the original repo: https://github.com/dolphin-zs/Doc2EDAG
$ unzip Data.zip
$ cd Data
# generate data with doc type (o2o, o2m, m2m) for better evaluation
$ python stat.py

# DuEE-fin
## If you want to win the test, you should check the codes and make further modifications,
## since each role may refer to multiple entities in DuEE-fin.
## Our PTPCG can help with this situation, all you need is to check the data preprocessing
## and check `predict_span_role()` method in `event_table.py`.
## We **do not** perform such magic tricks in the paper to make fair comparisons with Doc2EDAG and GIT.
$ # downloading datasets from https://aistudio.baidu.com/aistudio/competition/detail/65
$ cd Data/DuEEData  # paste train.json and dev.json into Data/DuEEData folder and run:
$ python build_data.py

📋To Reproduce Results in Paper

Doc2EDAG and GIT are already integrated in this repo, and more models are planned to be added.

If you want to reproduce the PTPCG results, or run other trials, please follow the instructions below.

Before running any bash script, please ensure bert_model has been correctly set.

Tip: At least 4 * NVIDIA V100 GPU (at least 16GB) cards are required to run Doc2EDAG models.

# run on ChFinAnn dataset
$ nohup bash scripts/run_doc2edag.sh 1>Logs/Doc2EDAG_reproduction.log 2>&1 &
$ tail -f Logs/Doc2EDAG_reproduction.log

# run on DuEE-fin dataset without trigger
$ nohup bash scripts/run_doc2edag_dueefin.sh.sh 1>Logs/Doc2EDAG_DuEE_fin.log 2>&1 &
$ tail -f Logs/Doc2EDAG_DuEE_fin.log

# run on DuEE-fin dataset with trigger
$ nohup bash scripts/run_doc2edag_dueefin_withtgg.sh 1>Logs/Doc2EDAG_DuEE_fin_with_trigger.log 2>&1 &
$ tail -f Logs/Doc2EDAG_DuEE_fin_with_trigger.log

Tip: At least 4 * NVIDIA V100 GPU (32GB) cards are required to run GIT models.

# run on ChFinAnn dataset
$ nohup bash scripts/run_git.sh 1>Logs/GIT_reproduction.log 2>&1 &
$ tail -f Logs/GIT_reproduction.log

# run on DuEE-fin dataset without trigger
$ nohup bash scripts/run_git_dueefin.sh 1>Logs/GIT_DuEE_fin.log 2>&1 &
$ tail -f Logs/GIT_DuEE_fin.log

# run on DuEE-fin dataset with trigger
$ nohup bash scripts/run_git_dueefin_withtgg.sh 1>Logs/GIT_DuEE_fin_with_trigger.log 2>&1 &
$ tail -f Logs/GIT_DuEE_fin_with_trigger.log

Tip: At least 1 * 1080Ti (at least 9GB) card is required to run PTPCG.

Default: |R| = 1, which means only the first (pseudo) trigger is selected.

# run on ChFinAnn dataset (to reproduce |R|=1 results in Table 1 of the PTPCG paper)
$ nohup bash scripts/run_ptpcg.sh 1>Logs/PTPCG_R1_reproduction.log 2>&1 &
$ tail -f Logs/PTPCG_R1_reproduction.log

# run on DuEE-fin dataset without annotated trigger (to reproduce |R|=1, Tgg=× results in Table 3 of the PTPCG paper)
$ nohup bash scripts/run_ptpcg_dueefin.sh 1>Logs/PTPCG_P1-DuEE_fin.log 2>&1 &
$ tail -f Logs/PTPCG_P1-DuEE_fin.log

# run on DuEE-fin dataset with annotated trigger and without pseudo trigger (to reproduce |R|=0, Tgg=√ results in Table 3 of the PTPCG paper)
$ nohup bash scripts/run_ptpcg_dueefin_withtgg.sh 1>Logs/PTPCG_T1-DuEE_fin.log 2>&1 &
$ tail -f Logs/PTPCG_T1-DuEE_fin.log

# run on DuEE-fin dataset with annotated trigger and one pseudo trigger (to reproduce |R|=1, Tgg=√ results in Table 3 of the PTPCG paper)
$ nohup bash scripts/run_ptpcg_dueefin_withtgg_withptgg.sh 1>Logs/PTPCG_P1T1-DuEE_fin.log 2>&1 &
$ tail -f Logs/PTPCG_P1T1-DuEE_fin.log
#PseudoTgg Setting Log Task Dump
1 189Cloud 189Cloud 189Cloud

Explainations on PTPCG hyperparameters in the executable script:

# whether to use max clique decoding strategy, brute-force if set to False
max_clique_decode = True
# number of triggers when training, to make all arguments as pseudo triggers, set to higher numbers like `10`
num_triggers = 1
# number of triggers when evaluating, set to `-1` to make all arguments as pseudo triggers
eval_num_triggers = 1
# put additional pseudo triggers into the graph, make full use of the pseudo triggers
with_left_trigger = True
# make the trigger graph to be directed
directed_trigger_graph = True
# run mode is used in `dee/tasks/dee_task.py/DEETaskSetting`
run_mode = 'full'
# at least one combination (see paper for more information)
at_least_one_comb = True
# whether to include regex matched entities
include_complementary_ents = True
# event schemas, check `dee/event_types` for all support schemas
event_type_template = 'zheng2019_trigger_graph'

⚽Find Pseudo Triggers

Please check Data/trigger.py for more details. In general, you should first convert your data into acceptable format (like typed_train.json after building ChFinAnn).

Then, you can run the command below to generate event schemas with pseudo triggers and importance scores:

$ cd Data
$ python trigger.py <max number of pseudo triggers>

📚Instructions

🙋FAQ

📜Citation

This work has been accepted to IJCAI'22, please cite the paper if you use PTPCG or this repository in your research. Thank you very much 😉

@inproceedings{ijcai2022p632,
  title     = {Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph},
  author    = {Zhu, Tong and Qu, Xiaoye and Chen, Wenliang and Wang, Zhefeng and Huai, Baoxing and Yuan, Nicholas and Zhang, Min},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {4552--4558},
  year      = {2022},
  month     = {7},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2022/632},
  url       = {https://doi.org/10.24963/ijcai.2022/632},
}

🔑Licence

MIT Licence

✨UPDATES

🤘Furthermore

This repo is still under development. If you find any bugs, don't hesitate to drop us an issue.

Thanks~