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KoCoNovel: Annotated Dataset of Character Coreference in Korean Novels

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arxiv 2404.01140 v2 pith:J2YJ3SCB submitted 2024-04-01 cs.CL

classification cs.CL
keywords coreferencekoconovelkoreancharacterliterarydatasettextsnovels
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we present KoCoNovel, a novel character coreference dataset derived from Korean literary texts, complete with detailed annotation guidelines. Comprising 178K tokens from 50 modern and contemporary novels, KoCoNovel stands as one of the largest public coreference resolution corpora in Korean, and the first to be based on literary texts. KoCoNovel offers four distinct versions to accommodate a wide range of literary coreference analysis needs. These versions are designed to support perspectives of the omniscient author or readers, and to manage multiple entities as either separate or overlapping, thereby broadening its applicability. One of KoCoNovel's distinctive features is that 24% of all character mentions are single common nouns, lacking possessive markers or articles. This feature is particularly influenced by the nuances of Korean address term culture, which favors the use of terms denoting social relationships and kinship over personal names. In experiments with a BERT-based coreference model, we observe notable performance enhancements with KoCoNovel in character coreference tasks within literary texts, compared to a larger non-literary coreference dataset. Such findings underscore KoCoNovel's potential to significantly enhance coreference resolution models through the integration of Korean cultural and linguistic dynamics.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Findings of the Fifth Shared Task on Multilingual Coreference Resolution: Expanding Datasets for Long-Range Entities

    cs.CL 2026-05 accept novelty 4.0 of 10

    The 2026 multilingual coreference shared task expanded to 27 datasets in 19 languages with a focus on long-range entities; traditional systems led but LLMs showed notable potential.

  2. Findings of the Fifth Shared Task on Multilingual Coreference Resolution: Expanding Datasets for Long-Range Entities

    cs.CL 2026-05 accept novelty 4.0 of 10

    On CorefUD 1.4 with new long-range datasets, CorPipeEnsemble leads at 77.11 CoNLL F1 while the best fine-tuned LLM reaches 74.32, narrowing the prior gap.

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