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The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization

  • Datos identificativos

    Identificador: imarina:9287297
    Autores:
    Pilan, IldikoLison, Pierreovrelid, LiljaPapadopoulou, AnthiSanchez, DavidBatet, Montserrat
    Resumen:
    We present a novel benchmark and associated evaluation metrics for assessing the performance of text anonymization methods. Text anonymization, defined as the task of editing a text document to prevent the disclosure of personal information, currently suffers from a shortage of privacy-oriented annotated text resources, making it difficult to properly evaluate the level of privacy protection offered by various anonymization methods. This paper presents TAB (Text Anony-mization Benchmark), a new, open-source annotated corpus developed to address this shortage. The corpus comprises 1,268 English-language court cases from the European Court of Human Rights (ECHR) enriched with comprehensive annotations about the personal information appearing in each document, including their semantic category, identifier type, confidential attributes, and co-reference relations. Compared with previous work, the TAB corpus is designed to go beyond traditional de-identification (which is limited to the detection of predefined semantic categories), and explicitly marks which text spans ought to be masked in order to conceal the identity of the person to be protected. Along with presenting the corpus and its annotation layers, we also propose a set of evaluation metrics that are specifically tailored toward measuring the performance of text anonymization, both in terms of privacy protection and utility preservation. We illustrate the use of the benchmark and the proposed metrics by assessing the empirical performance of several baseline text anonymization models. The full corpus along with its privacy-oriented annotation guidelines, evaluation scripts, and baseline models are available on: https://github.com /NorskRegnesentral/text-anonymization-benchmark.
  • Otros:

    Autor según el artículo: Pilan, Ildiko; Lison, Pierre; ovrelid, Lilja; Papadopoulou, Anthi; Sanchez, David; Batet, Montserrat
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Batet Sanromà, Montserrat / Sánchez Ruenes, David
    Resumen: We present a novel benchmark and associated evaluation metrics for assessing the performance of text anonymization methods. Text anonymization, defined as the task of editing a text document to prevent the disclosure of personal information, currently suffers from a shortage of privacy-oriented annotated text resources, making it difficult to properly evaluate the level of privacy protection offered by various anonymization methods. This paper presents TAB (Text Anony-mization Benchmark), a new, open-source annotated corpus developed to address this shortage. The corpus comprises 1,268 English-language court cases from the European Court of Human Rights (ECHR) enriched with comprehensive annotations about the personal information appearing in each document, including their semantic category, identifier type, confidential attributes, and co-reference relations. Compared with previous work, the TAB corpus is designed to go beyond traditional de-identification (which is limited to the detection of predefined semantic categories), and explicitly marks which text spans ought to be masked in order to conceal the identity of the person to be protected. Along with presenting the corpus and its annotation layers, we also propose a set of evaluation metrics that are specifically tailored toward measuring the performance of text anonymization, both in terms of privacy protection and utility preservation. We illustrate the use of the benchmark and the proposed metrics by assessing the empirical performance of several baseline text anonymization models. The full corpus along with its privacy-oriented annotation guidelines, evaluation scripts, and baseline models are available on: https://github.com /NorskRegnesentral/text-anonymization-benchmark.
    Áreas temáticas: Linguistics and language Linguistics Language and linguistics Language & linguistics Filologia, lingüística i sociolingüística Computer science, interdisciplinary applications Computer science, artificial intelligence Computer science applications Ciencias sociales Ciencias humanas Ciência da computação Artificial intelligence Applied linguistics
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: montserrat.batet@urv.cat david.sanchez@urv.cat
    Identificador del autor: 0000-0001-8174-7592 0000-0001-7275-7887
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://direct.mit.edu/coli/article/48/4/1053/112770/The-Text-Anonymization-Benchmark-TAB-A-Dedicated
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Computational Linguistics. 48 (4): 1053-1101
    Referencia de l'ítem segons les normes APA: Pilan, Ildiko; Lison, Pierre; ovrelid, Lilja; Papadopoulou, Anthi; Sanchez, David; Batet, Montserrat (2022). The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization. Computational Linguistics, 48(4), 1053-1101. DOI: 10.1162/coli_a_00458
    DOI del artículo: 10.1162/coli_a_00458
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Applied Linguistics,Artificial Intelligence,Computer Science Applications,Computer Science, Artificial Intelligence,Computer Science, Interdisciplinary Applications,Language & Linguistics,Language and Linguistics,Linguistics,Linguistics and Language
    Linguistics and language
    Linguistics
    Language and linguistics
    Language & linguistics
    Filologia, lingüística i sociolingüística
    Computer science, interdisciplinary applications
    Computer science, artificial intelligence
    Computer science applications
    Ciencias sociales
    Ciencias humanas
    Ciência da computação
    Artificial intelligence
    Applied linguistics
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