Extracting Entities and Events as a Single Task Using a Transition-Based Neural Model, code for IJCAI 2019 paper.
- DyNET 2.1
- Pyyaml 5.1
- gensim 3.7.3
- Pytorch 1.1.0
- pytorch-transformer 1.2.0
- flair 0.4.3
ACE2005 https://catalog.ldc.upenn.edu/ldc2006t06
We can not provide full ACE2005 data files due to LDC license, instead, sample JSON files are given in data_files/samples/ for reference.
To facilitate follow up research, we list documents split of train/dev/test in data_files/doc_split/ which is provided by Thien Huu Nguyen et al [2016]
data_config.yaml(for locating file paths)joint_config.yaml(for parameters tunning)
-
Put glove.6B.100d.txt in
data_files/glove_emb/ -
Then make vocabulary and pickle instances by:
python preprocess.py
- (Optional) Generate BERT Embeddings (bert-base-uncased):
python gen_bert_emb.py
Note that if you don`t use BERT, set use_sentence_vec to false in joint_config.yaml.
It performs poorly for argument roles without BERT embeddings (around 45% F-scores on test set). If you don`t want use BERT, consider incorporating dependency features as in Dependency-Bridge (Sha et al [2018]).
python train.py
- Thien Huu Nguyen, Kyunghyun Cho, and Ralph Grishman. Joint Event Extraction via Recurrent Neural Networks, NAACL, 2016
- Lei Sha, Feng Qian, Baobao Chang, and Zhifang Sui. Jointly extracting event triggers and arguments by dependency-bridge rnn and tensor-based argument interaction, AAAI, 2018
@inproceedings{ijcai2019-753,
title = {Extracting Entities and Events as a Single Task Using a Transition-Based Neural Model},
author = {Zhang, Junchi and Qin, Yanxia and Zhang, Yue and Liu, Mengchi and Ji, Donghong},
booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on
Artificial Intelligence, {IJCAI-19}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
pages = {5422--5428},
year = {2019},
month = {7},
doi = {10.24963/ijcai.2019/753},
url = {https://doi.org/10.24963/ijcai.2019/753},
}