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    <titleInfo>
        <title>Extracting Relations from Ecclesiastical Cultural Heritage Texts</title>
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    <name type="personal">
        <namePart type="given">Giulia</namePart>
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        <name type="personal">
            <namePart type="given">Mika</namePart>
            <namePart type="family">Hämäläinen</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Emily</namePart>
            <namePart type="family">Öhman</namePart>
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        <name type="personal">
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            <namePart type="family">Miyagawa</namePart>
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        <name type="personal">
            <namePart type="given">Khalid</namePart>
            <namePart type="family">Alnajjar</namePart>
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            <namePart type="given">Yuri</namePart>
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    <abstract>Motivated by the increasing volume of data and the necessity of getting valuable insights, this research describes the process of extracting entities and relations from Italian texts in the context of ecclesiastical cultural heritage data. Named Entity Recognition (NER) and Relation Extraction (RE) are paramount tasks in Natural Language Processing. This paper presents a traditional methodology based on a two-step procedure: firstly, a custom model for Named Entity Recognition extracts entities from data, and then, a multi-input neural network model is trained to perform Relation Classification as a multi-label classification problem. Data are provided by IDS&amp;Unitelm (technological partner of the IT Services and National Office for Ecclesiastical Cultural Heritage and Religious Buildings of CEI, the Italian Episcopal Conference) and concerns biographical texts of 9,982 entities of type person, which can be accessed by the online portal BeWeb. This approach aims to enhance the organization and accessibility of ecclesiastical cultural heritage data, offering deeper insights into historical biographical records.</abstract>
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            <start>41</start>
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%0 Conference Proceedings
%T Extracting Relations from Ecclesiastical Cultural Heritage Texts
%A Cruciani, Giulia
%Y Hämäläinen, Mika
%Y Öhman, Emily
%Y Miyagawa, So
%Y Alnajjar, Khalid
%Y Bizzoni, Yuri
%S Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, USA
%F cruciani-2024-extracting
%X Motivated by the increasing volume of data and the necessity of getting valuable insights, this research describes the process of extracting entities and relations from Italian texts in the context of ecclesiastical cultural heritage data. Named Entity Recognition (NER) and Relation Extraction (RE) are paramount tasks in Natural Language Processing. This paper presents a traditional methodology based on a two-step procedure: firstly, a custom model for Named Entity Recognition extracts entities from data, and then, a multi-input neural network model is trained to perform Relation Classification as a multi-label classification problem. Data are provided by IDS&Unitelm (technological partner of the IT Services and National Office for Ecclesiastical Cultural Heritage and Religious Buildings of CEI, the Italian Episcopal Conference) and concerns biographical texts of 9,982 entities of type person, which can be accessed by the online portal BeWeb. This approach aims to enhance the organization and accessibility of ecclesiastical cultural heritage data, offering deeper insights into historical biographical records.
%U https://aclanthology.org/2024.nlp4dh-1.5
%P 41-50

Markdown (Informal)

[Extracting Relations from Ecclesiastical Cultural Heritage Texts](https://aclanthology.org/2024.nlp4dh-1.5) (Cruciani, NLP4DH 2024)

• Extracting Relations from Ecclesiastical Cultural Heritage Texts (Cruciani, NLP4DH 2024)

ACL

• Giulia Cruciani. 2024. Extracting Relations from Ecclesiastical Cultural Heritage Texts. In Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities, pages 41–50, Miami, USA. Association for Computational Linguistics.