@inproceedings{silva-moro-2024-pportal,
    title = "{PPORTAL}{\_}ner: An Annotated Corpus of {P}ortuguese Literary Entities",
    author = "Silva, Mariana O.  and
      Moro, Mirella M.",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1132",
    pages = "12927--12937",
    abstract = "The intersection of natural language processing (NLP) and literary analysis has yielded valuable insights and applications across various languages. However, the scarcity of labeled data tailored for Portuguese literary texts poses a notable challenge. To address this gap, we present the PPORTAL{\_}ner corpus, an annotated dataset that simplifies the development of Named Entity Recognition (NER) models specifically adapted for Portuguese literary works. Our corpus includes annotations of PER, LOC, GPE, ORG, and DATE entities within a diverse set of 25 literary texts. Annotation of the corpus involved a two-step process: initial pre-annotation using a pre-trained spaCy model followed by correction and refinement using the Prodigy annotation tool. With a total of 125,059 tokens and 5,266 annotated entities, PPORTAL{\_}ner corpus significantly enriches the landscape of resources available for computational literary analysis in Portuguese. This paper details the annotation methodology, guidelines, and dataset statistics while also evaluating four NER models over the PPORTAL{\_}ner corpus. Our evaluation analysis reveals that fine-tuning on domain-specific data significantly improves NER model performance, demonstrating the value of the PPORTAL{\_}ner corpus for developing domain-specific language models.",
}

The intersection of natural language processing (NLP) and literary analysis has yielded valuable insights and applications across various languages. However, the scarcity of labeled data tailored for Portuguese literary texts poses a notable challenge. To address this gap, we present the PPORTAL{\_}ner corpus, an annotated dataset that simplifies the development of Named Entity Recognition (NER) models specifically adapted for Portuguese literary works. Our corpus includes annotations of PER, LOC, GPE, ORG, and DATE entities within a diverse set of 25 literary texts. Annotation of the corpus involved a two-step process: initial pre-annotation using a pre-trained spaCy model followed by correction and refinement using the Prodigy annotation tool. With a total of 125,059 tokens and 5,266 annotated entities, PPORTAL{\_}ner corpus significantly enriches the landscape of resources available for computational literary analysis in Portuguese. This paper details the annotation methodology, guidelines, and dataset statistics while also evaluating four NER models over the PPORTAL{\_}ner corpus. Our evaluation analysis reveals that fine-tuning on domain-specific data significantly improves NER model performance, demonstrating the value of the PPORTAL{\_}ner corpus for developing domain-specific language models.
Silva, Mariana O. Moro, Mirella M.
PPORTAL_ner: An Annotated Corpus of Portuguese Literary Entities