mattmdjaga/text-anonymization-benchmark-train

Dataset card for Text Anonymization Benchmark (TAB) train

Table of Contents

• —Table of Contents
• —Dataset Description
• —Dataset Summary
• —Supported Tasks and Leaderboards
• —Languages
• —Dataset Structure
• —Data Instances
• —Data Fields
• —Data Splits
• —Dataset Creation
• —Curation Rationale
• —Source Data
• —Annotations
• —Personal and Sensitive Information
• —Considerations for Using the Data
• —Social Impact of Dataset
• —Discussion of Biases
• —Other Known Limitations
• —Additional Information
• —Dataset Curators
• —Licensing Information
• —Citation Information
• —Contributions

Dataset Description

• —Homepage:
• —Repository:
• —Paper:
• —Leaderboard:
• —Point of Contact:

Dataset Summary

This is the training split of the Text Anonymisation Benchmark. As the title says it's a dataset focused on text anonymisation, specifcially European Court Documents, which contain labels by mutltiple annotators.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

[More Information Needed]

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

TAB is released under an MIT License. The MIT License is a short and simple permissive license allowing both commercial and non-commercial use of the software.

Citation Information

[More Information Needed]

Contributions

@article{DBLP:journals/corr/abs-2202-00443,
  author       = {Ildik{\'{o}} Pil{\'{a}}n and
                  Pierre Lison and
                  Lilja {\O}vrelid and
                  Anthi Papadopoulou and
                  David S{\'{a}}nchez and
                  Montserrat Batet},
  title        = {The Text Anonymization Benchmark {(TAB):} {A} Dedicated Corpus and
                  Evaluation Framework for Text Anonymization},
  journal      = {CoRR},
  volume       = {abs/2202.00443},
  year         = {2022},
  url          = {https://arxiv.org/abs/2202.00443},
  eprinttype    = {arXiv},
  eprint       = {2202.00443},
  timestamp    = {Wed, 09 Feb 2022 15:43:35 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2202-00443.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}