Cite (Informal): Corpus Annotation Graph Builder (CAG): An Architectural Framework to Create and Annotate a Multi-source Graph (El Baff et al., EACL 2023) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Video: = "Corpus Annotation Graph Builder (): An Architectural Framework to Create and Annotate a Multi-source Graph",īooktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", Association for Computational Linguistics. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 248–255, Dubrovnik, Croatia. Corpus Annotation Graph Builder (CAG): An Architectural Framework to Create and Annotate a Multi-source Graph. Anthology ID: 2023.eacl-demo.28 Volume: Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations Month: May Year: 2023 Address: Dubrovnik, Croatia Venue: EACL SIG: Publisher: Association for Computational Linguistics Note: Pages: 248–255 Language: URL: DOI: 10.18653/v1/2023.eacl-demo.28 Bibkey: el-baff-etal-2023-corpus Cite (ACL): Roxanne El Baff, Tobias Hecking, Andreas Hamm, Jasper W. Code and resources are publicly available on GitHub, and downloadable via PyPi with the command pip install cag. The resulting graphs can be used for further analyses across multiple downstream tasks (e.g., node classification). 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). Applications build graphs in an ad-hoc fashion, usually tailored to specific use cases, limiting their reusability. Abstract Graphs are a natural representation of complex data as their structure allows users to discover (often implicit) relations among the nodes intuitively.
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