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        <title>SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling</title>
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    <name type="personal">
        <namePart type="given">Matthias</namePart>
        <namePart type="family">Hartung</namePart>
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        <namePart type="given">Hendrik</namePart>
        <namePart type="family">ter Horst</namePart>
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        <namePart type="given">Tim</namePart>
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    <name type="personal">
        <namePart type="given">Roman</namePart>
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            <title>Proceedings of ACL 2018, System Demonstrations</title>
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            <namePart type="family">Liu</namePart>
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        <name type="personal">
            <namePart type="given">Thamar</namePart>
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    <abstract>Supervised machine learning algorithms require training data whose generation for complex relation extraction tasks tends to be difficult. Being optimized for relation extraction at sentence level, many annotation tools lack in facilitating the annotation of relational structures that are widely spread across the text. This leads to non-intuitive and cumbersome visualizations, making the annotation process unnecessarily time-consuming. We propose SANTO, an easy-to-use, domain-adaptive annotation tool specialized for complex slot filling tasks which may involve problems of cardinality and referential grounding. The web-based architecture enables fast and clearly structured annotation for multiple users in parallel. Relational structures are formulated as templates following the conceptualization of an underlying ontology. Further, import and export procedures of standard formats enable interoperability with external sources and tools.</abstract>
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%0 Conference Proceedings
%T SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling
%A Hartung, Matthias
%A ter Horst, Hendrik
%A Grimm, Frank
%A Diekmann, Tim
%A Klinger, Roman
%A Cimiano, Philipp
%Y Liu, Fei
%Y Solorio, Thamar
%S Proceedings of ACL 2018, System Demonstrations
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F hartung-etal-2018-santo
%X Supervised machine learning algorithms require training data whose generation for complex relation extraction tasks tends to be difficult. Being optimized for relation extraction at sentence level, many annotation tools lack in facilitating the annotation of relational structures that are widely spread across the text. This leads to non-intuitive and cumbersome visualizations, making the annotation process unnecessarily time-consuming. We propose SANTO, an easy-to-use, domain-adaptive annotation tool specialized for complex slot filling tasks which may involve problems of cardinality and referential grounding. The web-based architecture enables fast and clearly structured annotation for multiple users in parallel. Relational structures are formulated as templates following the conceptualization of an underlying ontology. Further, import and export procedures of standard formats enable interoperability with external sources and tools.
%R 10.18653/v1/P18-4012
%U https://aclanthology.org/P18-4012
%U https://doi.org/10.18653/v1/P18-4012
%P 68-73

Markdown (Informal)

[SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling](https://aclanthology.org/P18-4012) (Hartung et al., ACL 2018)

• SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling (Hartung et al., ACL 2018)

ACL

• Matthias Hartung, Hendrik ter Horst, Frank Grimm, Tim Diekmann, Roman Klinger, and Philipp Cimiano. 2018. SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling. In Proceedings of ACL 2018, System Demonstrations, pages 68–73, Melbourne, Australia. Association for Computational Linguistics.