CNSynSetE

Paper title: A Bilateral Context and Filtering Strategy based Approach to Chinese Entity Synonym Set Expansion
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This resource includes datasets, source code (training,evaluation, and prediction) for the paper: [A Bilateral Context and Filtering Strategy-based Approach to Chinese Entity Synonym Set Expansion].

Details about the resource, such as datasets and source code, are available at (http://cslab.ahpu.edu.cn/CNSynSetExpan/).

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Dependencies

Python 3 with NumPy
gensim
sklearn
scikit-opt
PyTorch
tqdm
tensorboardX
networkx
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Training

For SoGouCA(SGSynSetTra)

Setting the [ini_parameters.ini] as follows:

[ini_parameters_mode]
##mode of the project: train,eval_opt, and test
mode = train

[ini_parameters_train]
##train on RTX6000 with GPU-RAM=24GB
##dataset: training the model for BaiDu or SoGouCA respectively
##The trained models are saved in runs->snapshots_trianed_best->SoGouCA or runs->snapshots_trianed_best->Baidu
##The trained results are saved in runs->training_log_mu0.0_SoGouCA or runs->training_log_mu0.0_BaiDu
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##following is the ini_parameters_train for SoGouCA
dataset=SoGouCA

##mu: using 0.0-0.9 to train the model respectively.
mu=[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]

##delta: default=0.2 other values will evaluate in the eval_opt step
delta=0.2

##kappa:default=0.5 other values will evaluate in the eval_opt step
kappa=0.5

##lamb:default=0.5 other values will evaluate in the eval_opt step
lamb=0.5

Runing the script main.py

For BaiDu(BDSynSetTra)

Setting the [ini_parameters.ini] as follows:

[ini_parameters_mode]
##mode of the project: train, eval_opt, and test
mode = train

[ini_parameters_train]
##train on RTX6000 with GPU-RAM=24GB
##dataset: training the model for BaiDu or SoGouCA respectively
##The trained models are saved in runs->snapshots_trianed_best->SoGouCA or runs->snapshots_trianed_best->Baidu
##The trained results are saved in runs->training_log_mu0.0_SoGouCA or runs->training_log_mu0.0_BaiDu
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##following is the ini_parameters_train for BaiDu
dataset=BaiDu

##mu: using 0.0-0.9 to train the model respectively
mu=[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]

##delta:default=0.2 other values will evaluate in the eval_opt step
delta=0.2

##kappa:default=0.6 other values will evaluate in the eval_opt step
kappa=0.6

##lamb:default=0.5 other values will evaluate in the eval_opt step
lamb=0.5

Runing the script main.py

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Evaluation

For SoGouCA(SGSynSetTra)

Setting the [ini_parameters.ini] as follows:

[ini_parameters_mode]
##mode of the project: train, eval_opt, and test
mode = eval_opt

[ini_parameters_evaluation_opt]
##Using the PSO to optimal the best evaluation parameters for the project
##The evaluation parameter results are saved in runs->evaluation_log_BaiDu or runs->evaluation_log_SoGouCA

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##following is the ini_parameters_evaluation_opt for SoGouCA

###mutil_processNum, type=int; the number of the parallel processes. 1 denotes no parallel computing, beger than 1 denotes parallel computing
###For SoGouCA dataset, mutil_processNum=8, GPU-RAM=24GB; For Baidu dataset, mutil_processNum=3, GPU-RAM=24GB
mutil_processNum=8

##dataset, BaiDu or SoGouCA
dataset=SoGouCA

##mu: the lp and up limit of the mu
mu=[0.1,0.9]

##delta: the lp and up limit of the delta
delta=[0.1,0.9]

##kappa: the lp and up limit of the kappa
kappa=[0.1,0.9]

##lamb: the lp and up limit of the lamb
lamb=[0.1,0.9]

Runing the script main.py

For BaiDu(BDSynSetTra)

Setting the [ini_parameters.ini] as follows:

[ini_parameters_mode]
##mode of the project: train, eval_opt, and test
mode = eval_opt

[ini_parameters_evaluation_opt]
##Using the PSO to optimal the best evaluation parameters for the project
##The evaluation parameter results are saved in runs->evaluation_log_BaiDu or runs->evaluation_log_SoGouCA

######################################################################################
##following is the ini_parameters_evaluation_opt for BaiDu

###mutil_processNum, type=int; the number of the parallel processes. 1 denotes no parallel computing, beger than 1 denotes parallel computing
###For SoGouCA dataset, mutil_processNum=8, GPU-RAM=24GB; For Baidu dataset, mutil_processNum=3, GPU-RAM=24GB
mutil_processNum=3

##dataset, BaiDu or SoGouCA
dataset=BaiDu

##mu: the lp and up limit of the mu
mu=[0.1,0.9]

##delta: the lp and up limit of the delta
delta=[0.1,0.9]

##kappa: the lp and up limit of the kappa
kappa=[0.1,0.9]

##lamb: the lp and up limit of the lamb
lamb=[0.1,0.9]

Runing the script main.py

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Prediction

For SoGouCA(SGSynSetTra)

Setting the [ini_parameters.ini] as follows:

[ini_parameters_mode]
##mode of the project: train, eval_opt, and test
mode = test

[ini_parameters_test]
##Predicting the project using the best parameters or other parameters
##The prediction results are saved in runs->testing_log_BaiDu or runs->testing_log_SoGouCA
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##following is the ini_parameters_test for SoGouCA

##dataset: Predicting the model for BaiDu or SoGouCA respectively
dataset=SoGouCA

##mu: chooseing one of a figure or a list from 0.0-0.9 to test the project
mu=[0.2]

##delta: chooseing one of a figure from 0.0-0.9 to test the project
delta=[0.1]

##kappa: chooseing one of a figure from 0.0-0.9 to test the project
kappa=[0.1]

##lamb: chooseing one of a figure from 0.0-0.9 to test the project
lamb=[0.6]

Runing the script main.py

For BaiDu(BDSynSetTra)

Setting the [ini_parameters.ini] as follows:

[ini_parameters_mode]
##mode of the project: train,eval_opt, and test
mode = test

[ini_parameters_test]
##Predicting the project using the best parameters or other parameters
##The prediction results are saved in runs->testing_log_BaiDu or runs->testing_log_SoGouCA

######################################################################################
##following is the ini_parameters_test for BaiDu

##dataset: Predicting the model for BaiDu or SoGouCA respectively
dataset=BaiDu

##mu: chooseing one of a figure from 0.0-0.9 to test the project
mu=[0.3]

##delta: chooseing one of a figure from 0.0-0.9 to test the project
delta=[0.7]

##kappa: chooseing one of a figure from 0.0-0.9 to test the project
kappa=[0.7]

##lamb: chooseing one of a figure from 0.0-0.9 to test the project
lamb=[0.2]

Runing the script main.py

References

If our paper and the above datasets and source code are useful for your research, please cite the following paper in your publication:

@article{Huang2022CNSynSetE,
title={A Bilateral Context and Filtering Strategy based Approach to Chinese Entity Synonym Set Expansion},
author={Subin Huang and Yu Xiu and Jun Li and Sanmin Liu and Chao Kong},
journal={Complex & Intelligent Systems},
year={2023}, dio = {https://doi.org/10.1007/s40747-023-01064-w} }