@inproceedings{nassajian-etal-2026-named,
    title = "Named Entity Recognition for {P}ersian Literary Text: A Case Study on The Little Prince",
    author = "Nassajian, Minoo  and
      Nivre, Joakim  and
      Zeman, Daniel",
    editor = "Zhao, Jin  and
      Post, Claire Benet  and
      Hoefer, Elizabeth",
    booktitle = "Proceedings of The Seventh International Workshop on Designing Meaning Representations ({DMR} 2026) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.dmr-1.5/",
    doi = "10.63317/2jo2u37hdcot",
    pages = "54--64",
    abstract = "Existing Persian named entity recognition (NER) research has focused predominantly on news and social media domains, leaving literary texts{---}with their distinct linguistic characteristics{---}virtually unexplored. This paper addresses this gap by developing a new literary NER corpus using the Persian translation of The Little Prince story and evaluating existing state-of-the-art Persian NER tools on this corpus, trained exclusively on news and social media corpora. Our analysis reveals significant performance degradation on literary text, identifying systematic errors related to narrative-specific entities, metaphorical language, and discourse structures that challenge conventional NER approaches."
}