daima2017 / ActiveEA

ActiveEA: Active Learning for Neural Entity Alignment

ActiveEA

Source code of paper "ActiveEA: Active Learning for Neural Entity Alignment", which has been accepted at EMNLP 2021.

Steps of reproducing the experiments:

• Step 1: Download and unzip the repo. Suppose it is put under /path_to_proj/ and we refer the directory under it with relative path.
• Step 2: If you need to run our strategies on RDGCN, you need to download the open word embedding file from wiki-news-300d-1M.vec and put the unzipped file under dataset/. Otherwise, skip this step (the size of unzipped word embedding file will be 2.26GB).
• Step 3: Install conda environment

cd /path_to_proj/al4ea/
conda env create -f environment.yml

• Step 4: Configure settings. The scripts to run are under scripts/run_strategies/ The default settings are set in task_settings.sh. Before you run any script, set proj_dir in the setting file firstly.

• Step 5: Run scripts:

  • For trials: customizing script task_runner_trial.sh.
  • Run experiments about the "overall performance on 15K data": task_runner_overall_perf.sh.
  • Run experiments about the "overall performance on 15K data": task_runner_overall_perf_100k.sh.
  • Run experiments about the "effect of bachelors": task_runner_effect_of_bachelor_percent.sh.
  • Run experiments about the "effectiveness of bachelor recognizer": intermediate results have been saved with the generated dataset of AL process.
  • Run experiments about the "sensitivity of parameters": task_runner_effect_of_alpha.sh and task_runner_effect_of_batchsize.sh.

The generated datasets by different AL strategies will be saved to dataset/ with naming pattern like dataset/${seed}/${task_group}/${dataset_name}/${strategy_name}. The evaluation results on test set will be saved to output/results/.

Acknowledgement

We implement the neural EA models by customizing source code of OpenEA.