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Pytorch implementation of Attention and Edge-Label Guided Graph Convolutional Networks for Named Entity Recognition

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AELGCN-NER

Pytorch implementation of Attention and Edge-Label Guided Graph Convolutional Networks for Named Entity Recognition

EMNLP 2022 Edge-Label Guided Graph Convolutional Networks for Named Entity Recognition

Model Architecture

Requirement

Python 3.7

Pytorch 1.4.0

Transformers 3.3.1

CUDA 10.1, 10.2

Performance

Model Dataset F1
Syn-LSTM-CRF Chinese 78.51
Syn-LSTM-CRF(Our Implementation) Chinese 79.10
BiLSTM-AELGCN-CRF Chinese 79.44
Our AELGNC Implementation Chinese 79.04
Model Dataset F1
Syn-LSTM-CRF Onotnotes 89.04
Syn-LSTM-CRF(Our Implementation) Onotnotes 89.13
BiLSTM-AELGCN-CRF Onotnotes 89.25
Our AELGNC Implementation Onotnotes 89.07

Running

Firstly, download the embedding files: glove.6B.100d.txt , cc.ca.300.vec, cc.es.300.vec, cc.zh.300.vec, and put the files in the data folder.

By default, the model eval our saved model (without BERT) on SemEval 2010 Task 1 Spanish dataset.

python main.py  

To train the model with other datasets:

python main.py --mode=train --dataset=ontonotes --embedding_file=glove.6B.100d.txt

For more detailed usage, please refer to the SynLSTM-for-NER project

Related Repo

The code are created based on the codes of the paper "Dependency-Guided LSTM-CRF Model for Named Entity Recognition", EMNLP 2019 "Better Feature Integration for Named Entity Recognition", NAACL 2021 "Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation", EMNLP 2020 "Attention Guided Graph Convolutional Networks for Relation Extraction", ACL 2019

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Pytorch implementation of Attention and Edge-Label Guided Graph Convolutional Networks for Named Entity Recognition

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