This codebase is partially based on neural_sequence_labeling
- Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
- Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi, Sho Yokoi, Tatsuki Kuribayashi, Ryuto Konno, Kentaro Inui
- In ACL 2020
- Conference paper: https://www.aclweb.org/anthology/2020.acl-main.575/
- arXiv version: https://arxiv.org/abs/2004.14514
@inproceedings{ouchi-etal-2020-instance,
title = "Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition",
author = "Ouchi, Hiroki and
Suzuki, Jun and
Kobayashi, Sosuke and
Yokoi, Sho and
Kuribayashi, Tatsuki and
Konno, Ryuto and
Inui, Kentaro",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.575",
pages = "6452--6459",
abstract = "Interpretable rationales for model predictions play a critical role in practical applications. In this study, we develop models possessing interpretable inference process for structured prediction. Specifically, we present a method of instance-based learning that learns similarities between spans. At inference time, each span is assigned a class label based on its similar spans in the training set, where it is easy to understand how much each training instance contributes to the predictions. Through empirical analysis on named entity recognition, we demonstrate that our method enables to build models that have high interpretability without sacrificing performance.",
}
- CPU
conda create -n instance-based-ner python=3.6
source activate instance-based-ner
conda install -c conda-forge tensorflow
pip install ujson tqdm
git clone https://github.com/cl-tohoku/instance-based-ner_dev.git
- GPU
conda create -n instance-based-ner python=3.6
source activate instance-based-ner
pip install tensorflow-gpu==1.10 ujson tqdm
git clone https://github.com/cl-tohoku/instance-based-ner_dev.git
./create_datasets.sh
python run_knn_models.py --mode cmd --config_file checkpoint_knn_conll2003_lstm-minus_batch8_keep07_0/config.json
- Training:
python train_knn_models.py --config_file data/config/config.knn.conll2003.json - Predicting with random training sentences:
python run_knn_models.py --config_file checkpoint_knn/conll2003/config.json --knn_sampling random --data_path data/conll2003/valid.json - Predicting with nearest training sentences:
python run_knn_models.py --config_file checkpoint_knn/conll2003/config.json --knn_sampling random --data_path data/conll2003/valid.glove.50-nn.json
- Training:
python train_span_models.py --config_file data/config/config.span.conll2003.json - Predicting:
python run_span_models.py --config_file checkpoint_span/conll2003/config.json --data_path data/conll2003/valid.json
MIT License