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Proceedings of the 13th Conference on Language Resources and Evaluation (LRC 2022), pages 44774487 Marseille, 2025 June 2022 European Language Resources Association (ERA), licensed under CCBYNC4.0Bootstrapping

How to fill out bootstrapping text anonymization models

How to fill out bootstrapping text anonymization models

1. Collect a set of training data containing both original text and anonymized versions.
2. Train a model on the training data using a bootstrapping algorithm.
3. Use the trained model to generate anonymized versions of new text by applying the learned patterns.
4. Evaluate the performance of the model on a separate test dataset to ensure the anonymization is effective and accurate.

Who needs bootstrapping text anonymization models?

1. Researchers working with sensitive personal data who need to share or publish their findings while protecting privacy.
2. Companies or organizations handling customer data that want to comply with data protection regulations.
3. Journalists working on investigative reports where anonymity of sources is crucial.
4. Anyone concerned about protecting their own privacy in online communication.

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What is bootstrapping text anonymization models?

Bootstrapping text anonymization models involve training a model using limited labeled data and then using the model to label more data, improving the model iteratively.

Who is required to file bootstrapping text anonymization models?

Companies or researchers working on text anonymization projects may be required to file bootstrapping text anonymization models.

How to fill out bootstrapping text anonymization models?

To fill out bootstrapping text anonymization models, one needs to provide details on the training data, model architecture, labeling process, and evaluation metrics used.

What is the purpose of bootstrapping text anonymization models?

The purpose of bootstrapping text anonymization models is to improve the accuracy and efficiency of text anonymization tasks by leveraging existing data and models.

What information must be reported on bootstrapping text anonymization models?

Information such as training data sources, model hyperparameters, iterative labeling results, and final evaluation scores must be reported on bootstrapping text anonymization models.