KoCoNovel: Annotated Dataset of Character Coreference in Korean Novels

TL;DR

KoCoNovel is a large, annotated Korean literary dataset for character coreference, supporting diverse analysis perspectives and improving coreference resolution models by capturing cultural nuances.

Contribution

It introduces KoCoNovel, the first Korean literary coreference dataset with multiple annotation versions and cultural features, advancing Korean NLP research.

Findings

Enhanced coreference model performance with KoCoNovel

Supports multiple analysis perspectives and entity types

Highlights cultural influences on Korean coreference

Abstract

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.