GraphLit learns representations from ~20,000 dynamic heterogeneous character networks extracted from Project Gutenberg novels via masked graph autoencoders and outperforms text-only and graph-only baselines on 12 character-related tasks.
InFindings of the Association for Computational Linguistics: EMNLP 2024, pages 4471–4500, Miami, Florida, USA
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GraphLit: Learning Text-Enriched Dynamic Character Network Representations for Literary Study
GraphLit learns representations from ~20,000 dynamic heterogeneous character networks extracted from Project Gutenberg novels via masked graph autoencoders and outperforms text-only and graph-only baselines on 12 character-related tasks.