Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering
Paper accepted by NAACL-HLT 2019: NFETC-CLSC
- python 3.6.0
- tensorflow == 1.6.0
- hyperopt
- gensim
- sklearn
- pandas
Run pip install -r requirement.txt to satisfy the prerequisites.
Run ./download.sh to download the pre-trained word embeddings.
The preprocessed dataset can be download from Google Drive
Put the data under the ./data/ directory
Run python eval.py -m <model_name> -d <data_name> -r <runs> -p <number> -a <alpha>
The scores for each run and the average scores are also recorded in one log file stored in folder log
Available <data_name>: bbn , ontonotes
Available <model_name>: nfetc_bbn_NFETC_CLSC , nfetc_ontonotes_NFETC_CLSC
(which can be modified in model_param_space.py, the detailed hyper-parameter is in this file too)
Available <number> for noisy data: 5, 10, 15, 20, 25, 100
Available <number> for clean data: 500, 1000, 1500, 2000, 2500 (You need to prepare the training file as mentioned before)
<alpha> is the hierarchy loss factor, default == 0.0
If you found this codebase or our work useful, please cite:
@inproceedings{chen-etal-2019-improving,
title = "Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering",
author = "Chen, Bo and
Gu, Xiaotao and
Hu, Yufeng and
Tang, Siliang and
Hu, Guoping and
Zhuang, Yueting and
Ren, Xiang",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N19-1294",
pages = "2862--2872",
}
Note:
This code is based on the previous work by Peng Xu. Many thanks to Peng Xu.
Sincerely thanks Konstantinos Kamnitsas
for the guidance of the CLSC impelementation and the advice for the paper writting.