NFETC-CLSC

Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering

Paper accepted by NAACL-HLT 2019: NFETC-CLSC

Prerequisites

• python 3.6.0
• tensorflow == 1.6.0
• hyperopt
• gensim
• sklearn
• pandas

Run pip install -r requirement.txt to satisfy the prerequisites.

Dataset

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

Evaluation

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

Cite

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.