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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.

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