This repository provides utilities for anonymizing, pseudonymizing, and simplifying Dutch text using various NLP techniques. The main functionalities are implemented in the anonymization_utils.py file, with an example usage provided in the example.ipynb notebook.
- Anonymization: Replace sensitive entities in the text with generic placeholders.
- Pseudonymization: Replace sensitive entities with unique identifiers.
- Text Simplification: Simplify Dutch text using a pre-trained model.
- SARI Score Calculation: Evaluate the quality of text simplification.
- Entity Extraction: Extract named entities from the text.
- Entity Comparison: Compare entities between original and simplified texts.
- Score Plotting: Visualize the performance metrics.
To anonymize text, use the anonymize_text function:
from anonymization_utils import anonymize_text
text = "Your text here"
anonymized_text, analyzer_results = anonymize_text(text)
print(anonymized_text)To pseudonymize text, use the pseudonymize_text function:
from anonymization_utils import pseudonymize_text
pseudonymized_text = pseudonymize_text(text, analyzer_results)
print(pseudonymized_text)To simplify text, use the simplify_text function:
from anonymization_utils import simplify_text
simplified_text = simplify_text(anonymized_text)
print(simplified_text)To calculate the SARI score, use the calculate_sari_score function:
from anonymization_utils import calculate_sari_score
sari_score = calculate_sari_score(text, simplified_text)
print(sari_score)To extract entities from text, use the extract_entities function:
from anonymization_utils import extract_entities
entities = extract_entities(text)
print(entities)To compare entities between original and simplified texts, use the compare_entities function:
from anonymization_utils import compare_entities
precision, recall, f1, true_positives, false_positives, false_negatives = compare_entities(original_entities, simplified_entities)
print(f"Precision: {precision}, Recall: {recall}, F1 Score: {f1}")To plot the performance metrics, use the plot_scores function:
from anonymization_utils import plot_scores
anonymized_scores = [precision_anonymized, recall_anonymized, f1_anonymized, sari_score_anonymized]
pseudonymized_scores = [precision_pseudonymized, recall_pseudonymized, f1_pseudonymized, sari_score_pseudonymized]
plot_scores(anonymized_scores, pseudonymized_scores)To ensure the effectiveness and accuracy of our text processing techniques, we use several evaluation metrics, particularly in the context of text simplification and anonymization/pseudonymization:
- Precision: Indicates the accuracy of the entities identified by the model. High precision means that the model correctly identifies and anonymizes sensitive information without affecting irrelevant data.
- Recall: Reflects the model's ability to find all relevant instances in the text. High recall ensures that most, if not all, sensitive information is identified and anonymized.
- F1 Score: Balances precision and recall, providing a single metric that considers both the accuracy and completeness of the anonymization process. A high F1 score indicates a well-performing model that effectively anonymizes sensitive information while minimizing errors.
- SARI Score: Evaluates the quality of text simplification by comparing the simplified text to the original and reference texts. It helps assess how well the simplified text retains its meaning and readability after anonymization.
These metrics complement each other by providing a comprehensive evaluation of the model's performance in both simplifying and anonymizing text. Precision and recall focus on the accuracy and completeness of anonymization, while the F1 score offers a balanced view. The SARI score ensures that the simplified text remains clear and understandable, even after anonymization.
For a complete example, refer to the example.ipynb notebook in this repository. It demonstrates how to use the utilities step-by-step with sample text.
In this repository, we have provided a comprehensive set of tools for anonymizing, pseudonymizing, and simplifying Dutch text using advanced NLP techniques. By leveraging the functionalities in anonymization_utils.py, users can effectively protect sensitive information while maintaining the readability and integrity of the text. The example provided in example.ipynb demonstrates the practical application of these techniques, showcasing their effectiveness through various evaluation metrics.
These tools are particularly relevant in domains where privacy and data protection are paramount, such as healthcare, legal, and customer service industries. By ensuring that sensitive information is anonymized or pseudonymized, organizations can comply with data protection regulations while still utilizing valuable textual data for analysis and processing.
Overall, the combination of anonymization, pseudonymization, and text simplification techniques presented in this repository offers a robust solution for handling sensitive information in Dutch texts, making it a valuable resource for researchers and practitioners in the field of natural language processing and data privacy.
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