This is the codebase for the system demo EventPlus: A Temporal Event Understanding Pipeline in NAACL 2021.
Please refer to our paper for details. [PDF] [Talk] [Demo]
0 - Clone the codebase with all submodules
git clone --recurse-submodules https://github.com/PlusLabNLP/EventPlus.git
# or use following commands
git clone https://github.com/PlusLabNLP/EventPlus.git
git submodule init
git submodule update
1 - Environment Installation
Change prefix (last line) of env.yml to fit your path, then run
conda env create -f env.yml
conda activate event-pipeline
pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.2.4/en_ner_jnlpba_md-0.2.4.tar.gz
python -m spacy download en_core_web_sm
pip install git+https://github.com/hltcoe/PredPatt.git
2 - Download trained model for components
For component/BETTER module, download the trained model [Link], unzip and place it under component/BETTER/joint/worked_model_ace.
For component/TempRel module, download the trained model [Link], unzip and place it under component/TempRel/models.
For component/Duration module, download scripts zip file [Link], unzip and place it under component/Duration/scripts.
For component/NegationDetection module, download the trained model [Link], unzip and place is under component/NegationDetection/models
3 - In background: Run REST API for event duration detection module for faster processing
(optional) tmux new -s duration_rest_api
conda activate event-pipeline
cd component/REST_service
python main.py
(optional) exit tmux window
4 - Application 1: Raw Text Annotation. The input is a multiple line raw text file, and the output pickle and json file will be saved to designated paths
cd YOUR_PREFERRED_PATH/project
python APIs/test_on_raw_text.py -data YOUR_RAW_TEXT_FILE -save_path SAVE_PICKLE_PATH -save_path_json SAVE_JSON_PATH -negation_detection
5 - Application 2: Web App for Interaction and Visualization. A web app will be started and user can input a piece of text and get annotation result and visualization.
cd YOUR_PREFERRED_PATH/project
tmux new -s serve
python manage.py runserver 8080
The code for data processing and incorporating different components is in project/APIs/main.py. Please refer to README file of each component for more details about training and inference.
1- Event Extraction on ACE Ontology: component/BETTER
2- Joint Event Trigger and Temporal Relation Extraction: component/TempRel for inference, this codebase for training
3- Event Duration Detection: component/Duration
4- Negation and Speculation Cue Detection and Scope Resolution: component/NegationDetection
5- Biomedical Event Extraction: component/BioMedEventEx for inference, this codebase for training
If you are using the system on a machine with access to ISI shared NAS, you could directly activate environment and copy the code and start using it right away!
# 1 - Environment Installation: Activate existing environment
conda activate /nas/home/mingyuma/miniconda3/envs/event-pipeline-dev
# 2 - Prepare Components (Submodules): Copy the whole codebase
cp -R /nas/home/mingyuma/event-pipeline/event-pipeline-dev YOUR_PREFERRED_PATH
# 3 - In background: Run REST API for event duration detection module for faster processing
(optional) tmux new -s duration_rest_api
conda activate /nas/home/mingyuma/miniconda3/envs/event-pipeline-dev
cd component/REST_service
python main.py
(optional) exit tmux window
# To use it for raw text annotation or web app, please follow step 4 and 5 in quick start section.
Here are instruction of how to deploy the web application on an server
pip install uwsgi
If you met errors like error while loading shared libraries libssl.so.1.1, reference this link and do the following
export LD_LIBRARY_PATH=/nas/home/mingyuma/miniconda3/envs/event-pipeline/lib:$LD_LIBRARY_PATH
External port: 443 (for HTTPS)
Django will forward traffic from 443 port to internal 8080 port
Internal port
- 8080: run Django main process
- 17000: run service for duration (if we run a REST API for duration module, but now the newer version doesn't need such a separate service)
@inproceedings{ma-etal-2021-eventplus,
title = "{E}vent{P}lus: A Temporal Event Understanding Pipeline",
author = "Ma, Mingyu Derek and
Sun, Jiao and
Yang, Mu and
Huang, Kung-Hsiang and
Wen, Nuan and
Singh, Shikhar and
Han, Rujun and
Peng, Nanyun",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.naacl-demos.7",
pages = "56--65",
abstract = "We present EventPlus, a temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, event argument detection, event duration and temporal relation extraction. Event information, especially event temporal knowledge, is a type of common sense knowledge that helps people understand how stories evolve and provides predictive hints for future events. EventPlus as the first comprehensive temporal event understanding pipeline provides a convenient tool for users to quickly obtain annotations about events and their temporal information for any user-provided document. Furthermore, we show EventPlus can be easily adapted to other domains (e.g., biomedical domain). We make EventPlus publicly available to facilitate event-related information extraction and downstream applications.",
}