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        <title>Corpus Annotation Graph Builder (CAG): An Architectural Framework to Create and Annotate a Multi-source Graph</title>
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    <name type="personal">
        <namePart type="given">Roxanne</namePart>
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    <name type="personal">
        <namePart type="given">Tobias</namePart>
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    <name type="personal">
        <namePart type="given">Sabine</namePart>
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            <namePart type="given">Danilo</namePart>
            <namePart type="family">Croce</namePart>
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            <namePart type="given">Luca</namePart>
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    <abstract>Graphs are a natural representation of complex data as their structure allows users to discover (often implicit) relations among the nodes intuitively. Applications build graphs in an ad-hoc fashion, usually tailored to specific use cases, limiting their reusability. To account for this, we present the Corpus Annotation Graph (CAG) architectural framework based on a create-and-annotate pattern that enables users to build uniformly structured graphs from diverse data sources and extend them with automatically extracted annotations (e.g., named entities, topics). The resulting graphs can be used for further analyses across multiple downstream tasks (e.g., node classification). Code and resources are publicly available on GitHub, and downloadable via PyPi with the command pip install cag.</abstract>
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    <identifier type="doi">10.18653/v1/2023.eacl-demo.28</identifier>
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        <date>2023-05</date>
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%0 Conference Proceedings
%T Corpus Annotation Graph Builder (CAG): An Architectural Framework to Create and Annotate a Multi-source Graph
%A El Baff, Roxanne
%A Hecking, Tobias
%A Hamm, Andreas
%A Korte, Jasper W.
%A Bartsch, Sabine
%Y Croce, Danilo
%Y Soldaini, Luca
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F el-baff-etal-2023-corpus
%X Graphs are a natural representation of complex data as their structure allows users to discover (often implicit) relations among the nodes intuitively. Applications build graphs in an ad-hoc fashion, usually tailored to specific use cases, limiting their reusability. To account for this, we present the Corpus Annotation Graph (CAG) architectural framework based on a create-and-annotate pattern that enables users to build uniformly structured graphs from diverse data sources and extend them with automatically extracted annotations (e.g., named entities, topics). The resulting graphs can be used for further analyses across multiple downstream tasks (e.g., node classification). Code and resources are publicly available on GitHub, and downloadable via PyPi with the command pip install cag.
%R 10.18653/v1/2023.eacl-demo.28
%U https://aclanthology.org/2023.eacl-demo.28
%U https://doi.org/10.18653/v1/2023.eacl-demo.28
%P 248-255

Markdown (Informal)

[Corpus Annotation Graph Builder (CAG): An Architectural Framework to Create and Annotate a Multi-source Graph](https://aclanthology.org/2023.eacl-demo.28) (El Baff et al., EACL 2023)

• Corpus Annotation Graph Builder (CAG): An Architectural Framework to Create and Annotate a Multi-source Graph (El Baff et al., EACL 2023)

ACL

• Roxanne El Baff, Tobias Hecking, Andreas Hamm, Jasper W. Korte, and Sabine Bartsch. 2023. Corpus Annotation Graph Builder (CAG): An Architectural Framework to Create and Annotate a Multi-source Graph. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 248–255, Dubrovnik, Croatia. Association for Computational Linguistics.