@inproceedings{mccrae-etal-2024-teanga,
    title = "Teanga Data Model for Linked Corpora",
    author = "McCrae, John P.  and
      Rani, Priya  and
      Doyle, Adrian  and
      Stearns, Bernardo",
    editor = "Chiarcos, Christian  and
      Gkirtzou, Katerina  and
      Ionov, Maxim  and
      Khan, Fahad  and
      McCrae, John P.  and
      Ponsoda, Elena Montiel  and
      Chozas, Patricia Mart{\'\i}n",
    booktitle = "Proceedings of the 9th Workshop on Linked Data in Linguistics @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.ldl-1.9",
    pages = "66--74",
    abstract = "Corpus data is the main source of data for natural language processing applications, however no standard or model for corpus data has become predominant in the field. Linguistic linked data aims to provide methods by which data can be made findable, accessible, interoperable and reusable (FAIR). However, current attempts to create a linked data format for corpora have been unsuccessful due to the verbose and specialised formats that they use. In this work, we present the Teanga data model, which uses a layered annotation model to capture all NLP-relevant annotations. We present the YAML serializations of the model, which is concise and uses a widely-deployed format, and we describe how this can be interpreted as RDF. Finally, we demonstrate three examples of the use of the Teanga data model for syntactic annotation, literary analysis and multilingual corpora.",
}

Corpus data is the main source of data for natural language processing applications, however no standard or model for corpus data has become predominant in the field. Linguistic linked data aims to provide methods by which data can be made findable, accessible, interoperable and reusable (FAIR). However, current attempts to create a linked data format for corpora have been unsuccessful due to the verbose and specialised formats that they use. In this work, we present the Teanga data model, which uses a layered annotation model to capture all NLP-relevant annotations. We present the YAML serializations of the model, which is concise and uses a widely-deployed format, and we describe how this can be interpreted as RDF. Finally, we demonstrate three examples of the use of the Teanga data model for syntactic annotation, literary analysis and multilingual corpora.
McCrae, John P. Rani, Priya Doyle, Adrian Stearns, Bernardo
Teanga Data Model for Linked Corpora