https://github.com/nusnlp/ptrnetdecoding4jere

Code for modeling encoder-decoder architecture with pointer network for joint entity and relation extraction (AAAI 2020).
https://github.com/nusnlp/ptrnetdecoding4jere

Last synced: 17 days ago
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Code for modeling encoder-decoder architecture with pointer network for joint entity and relation extraction (AAAI 2020).

• Host: GitHub
• URL: https://github.com/nusnlp/ptrnetdecoding4jere
• Owner: nusnlp
• Created: 2019-11-11T05:30:43.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-02-28T03:08:23.000Z (almost 5 years ago)
• Last Synced: 2023-10-20T22:08:31.383Z (over 1 year ago)
• Language: Python
• Homepage:
• Size: 17.6 KB
• Stars: 100
• Watchers: 8
• Forks: 13
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# PtrNetDecoding4JERE

This repository contains the source code of the paper "Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction" published in AAAI 2020.

### Datasets ###

NYT24 and NYT29 datasets used for experiments in the paper can be downloaded from the following link:

https://drive.google.com/drive/folders/1RPD9kuHUHp4O3gQLLD1CgDPigAlRiR7L?usp=sharing

### Requirements ###

1) python3.5

2) pytorch 1.1.0

3) CUDA 8.0

### How to run ###

#### Word Decoding Model ####

python3.5 word_decoder.py gpu_id random_seed source_data_dir target_data_dit train/test

python3.5 word_decoder.py 0 1023 NYT29/ NYT29/word_decode_model train

python3.5 word_decoder.py 0 1023 NYT29/ NYT29/word_decode_model test

#### Pointer Network-based Decoding Model #### 

python3.5 ptrnet_decoder.py gpu_id random_seed source_data_dir target_data_dit train/test

python3.5 ptrnet_decoder.py 0 1023 NYT29/ NYT29/ptrnet_decode_model train

python3.5 ptrnet_decoder.py 0 1023 NYT29/ NYT29/ptrnet_decode_model test

### Publication ###

https://arxiv.org/abs/1911.09886

If you use the source code or models from this work, please cite our paper:

```

@inproceedings{nayak2019ptrnetdecoding,

  author    = {Nayak, Tapas and Ng, Hwee Tou},

  title     = {Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction},

  booktitle = {Proceedings of The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI)},

  year      = {2020}

}

```