<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="de-gibert-bonet-etal-2022-spanish">
    <titleInfo>
        <title>Spanish Datasets for Sensitive Entity Detection in the Legal Domain</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ona</namePart>
        <namePart type="family">de Gibert Bonet</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Aitor</namePart>
        <namePart type="family">García Pablos</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Montse</namePart>
        <namePart type="family">Cuadros</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Maite</namePart>
        <namePart type="family">Melero</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Thirteenth Language Resources and Evaluation Conference</title>
        </titleInfo>
        <originInfo>
            <publisher>European Language Resources Association</publisher>
            <place>
                <placeTerm type="text">Marseille, France</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The de-identification of sensible data, also known as automatic textual anonymisation, is essential for data sharing and reuse, both for research and commercial purposes. The first step for data anonymisation is the detection of sensible entities. In this work, we present four new datasets for named entity detection in Spanish in the legal domain. These datasets have been generated in the framework of the MAPA project, three smaller datasets have been manually annotated and one large dataset has been automatically annotated, with an estimated error rate of around 14%. In order to assess the quality of the generated datasets, we have used them to fine-tune a battery of entity-detection models, using as foundation different pre-trained language models: one multilingual, two general-domain monolingual and one in-domain monolingual. We compare the results obtained, which validate the datasets as a valuable resource to fine-tune models for the task of named entity detection. We further explore the proposed methodology by applying it to a real use case scenario.</abstract>
    <identifier type="citekey">de-gibert-bonet-etal-2022-spanish</identifier>
    <location>
        <url>https://aclanthology.org/2022.lrec-1.400</url>
    </location>
    <part>
        <date>2022-06</date>
        <extent unit="page">
            <start>3751</start>
            <end>3760</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Spanish Datasets for Sensitive Entity Detection in the Legal Domain
%A de Gibert Bonet, Ona
%A García Pablos, Aitor
%A Cuadros, Montse
%A Melero, Maite
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F de-gibert-bonet-etal-2022-spanish
%X The de-identification of sensible data, also known as automatic textual anonymisation, is essential for data sharing and reuse, both for research and commercial purposes. The first step for data anonymisation is the detection of sensible entities. In this work, we present four new datasets for named entity detection in Spanish in the legal domain. These datasets have been generated in the framework of the MAPA project, three smaller datasets have been manually annotated and one large dataset has been automatically annotated, with an estimated error rate of around 14%. In order to assess the quality of the generated datasets, we have used them to fine-tune a battery of entity-detection models, using as foundation different pre-trained language models: one multilingual, two general-domain monolingual and one in-domain monolingual. We compare the results obtained, which validate the datasets as a valuable resource to fine-tune models for the task of named entity detection. We further explore the proposed methodology by applying it to a real use case scenario.
%U https://aclanthology.org/2022.lrec-1.400
%P 3751-3760

Markdown (Informal)

[Spanish Datasets for Sensitive Entity Detection in the Legal Domain](https://aclanthology.org/2022.lrec-1.400) (de Gibert Bonet et al., LREC 2022)

• Spanish Datasets for Sensitive Entity Detection in the Legal Domain (de Gibert Bonet et al., LREC 2022)

ACL

• Ona de Gibert Bonet, Aitor García Pablos, Montse Cuadros, and Maite Melero. 2022. Spanish Datasets for Sensitive Entity Detection in the Legal Domain. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3751–3760, Marseille, France. European Language Resources Association.