@inproceedings{song-liu-2024-approaches,
    title = "Approaches and Challenges for Resolving Different Representations of Fictional Characters for {C}hinese Novels",
    author = "Song, Li  and
      Liu, Ying",
    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.125",
    pages = "1408--1421",
    abstract = "Due to the huge scale of literary works, automatic text analysis technologies are urgently needed for literary studies such as Digital Humanities. However, the domain-generality of existing NLP technologies limits their effectiveness on in-depth literary studies. It is valuable to explore how to adapt NLP technologies to the literary-specific tasks. Fictional characters are the most essential elements of a novel, and thus crucial to understanding the content of novels. The prerequisite of collecting a character{'}s information is to resolve its different representations. It is a specific problem of anaphora resolution which is a classical and open-domain NLP task. We adapt a state-of-the-art anaphora resolution model to resolve character representations in Chinese novels by making some modifications, and train a widely used BERT fine-tuned model for speaker extraction as assistance. We also analyze the challenges and potential solutions for character-resolution in Chinese novels according to the resolution results on a specific Chinese novel.",
}

Due to the huge scale of literary works, automatic text analysis technologies are urgently needed for literary studies such as Digital Humanities. However, the domain-generality of existing NLP technologies limits their effectiveness on in-depth literary studies. It is valuable to explore how to adapt NLP technologies to the literary-specific tasks. Fictional characters are the most essential elements of a novel, and thus crucial to understanding the content of novels. The prerequisite of collecting a character{'}s information is to resolve its different representations. It is a specific problem of anaphora resolution which is a classical and open-domain NLP task. We adapt a state-of-the-art anaphora resolution model to resolve character representations in Chinese novels by making some modifications, and train a widely used BERT fine-tuned model for speaker extraction as assistance. We also analyze the challenges and potential solutions for character-resolution in Chinese novels according to the resolution results on a specific Chinese novel.
Song, Li Liu, Ying
Approaches and Challenges for Resolving Different Representations of Fictional Characters for Chinese Novels