Artificial Relationships in Fiction: A Dataset for Advancing NLP in Literary Domains (2025)

Abstract: Relation extraction (RE) in fiction presents unique NLP challenges due to implicit, narrative-driven relationships. Unlike factual texts, fiction weaves complex connections, yet existing RE datasets focus on non-fiction. To address this, we introduce Artificial Relationships in Fiction (ARF), a synthetically annotated dataset for literary RE. Built from diverse Project Gutenberg fiction, ARF considers author demographics, publication periods, and themes. We curated an ontology for fiction-specific entities and relations, and using GPT-4o, generated artificial relationships to capture narrative complexity. Our analysis demonstrates its value for finetuning RE models and advancing computational literary studies. By bridging a critical RE gap, ARF enables deeper exploration of fictional relationships, enriching NLP research at the intersection of storytelling and AI-driven literary analysis.

## introduction relation extraction is a fundamental nlp task that identifies and categorizes semantic relationships between entities in text @xcite . while re has been extensively studied in structured domains like news articles and scientific literature @xcite , its application to fiction remains underexplored @xcite . fictional narratives present unique challenges due to their narrative-driven structures, implicit relationships, and varied linguistic styles that differ significantly from factual texts @xcite . to address this gap, we introduce artificial relationships in fiction (arf), a synthetically annotated dataset for re in literary texts. the dataset is constructed from a curated selection of real literary texts sourced from project gutenberg, with relationship annotations generated using gpt-4o. unlike traditional datasets that rely on manual annotation, arf leverages ai-assisted annotation to extract meaningful relationships within fictional narratives. this approach enables large-scale dataset creation while capturing the complexities of fictional interactions @xcite . our contributions include: (1) introducing a synthetically annotated dataset for re in fiction, (2) developing a systematic dataset creation methodology combining curated selection with language model-assisted relationship generation, and (3) providing a thorough dataset analysis along with example use cases demonstrating its potential for re research in fiction. the paper is structured as follows: section 2 reviews related work, section 3 outlines dataset creation, section 4 presents dataset analysis, section 5 explores evaluation methods and applications, and section 6 concludes with future research directions. ## related work research on re has traditionally focused on structured, factual texts. numerous datasets and approaches address newswire @xcite , biomedical @xcite , finance @xcite , legal @xcite , and scientific literature @xcite . for instance, the tacred dataset @xcite provides a large-scale corpus of annotated sentences for relation classification, while the ace dataset @xcite supports multilingual entity, relation, and event detection. shared tasks at semeval @xcite further drive benchmarks, enabling methods from featurebased learning @xcite to deep neural networks @xcite to advance re in nonfiction. despite these developments, fiction remains comparatively underexplored @xcite . unlike factual prose, fictional narratives often convey relationships implicitly through figurative language, complex story arcs, and evolv-ing character dynamics. early computational literary analysis studied character networks and narrative structures @xcite , while systems like booknlp @xcite aided entity extraction and coreference resolution. litbank @xcite annotates literary entities but lacks focus on fictional relationships. other fictionoriented datasets target social networks @xcite or characterization @xcite but do not systematically address re. limited re efforts in fiction, e.g., character-location associations @xcite @xcite @xcite , often have narrow scopes and lack comprehensive ontologies that capture the diverse range of fictional entities and relations @xcite . in response to this gap, recent work has expanded literary relationship analysis. for instance, @xcite introduced synthetic annotations of social networks in literary texts. however, such efforts typically focus on specific relationship types or small corpora. advancing re in fiction requires richer ontologies encompassing characters, settings, objects, abstract concepts, and thematic linkages @xcite . meanwhile, the use of large language models for generating synthetic training data has gained momentum as a means to overcome the scarcity and cost of human annotations @xcite . @xcite show how gpt-3.5 excels at few-shot re, highlighting the potential of llms for creative or domain-specific tasks. leveraging these models, researchers can create datasets that reflect the complexity of fictional narratives while still maintaining consistency and diversity in annotations. against this backdrop, we present the artificial relationships in fiction (arf) dataset, a synthetic resource for literary re. using fiction from project gutenberg and a tailored ontology, arf leverages gpt-4o to generate nuanced relationships, enriching resources and advancing research in nlp, storytelling, and computational literary analysis. ## dataset creation high-quality datasets are vital for nlp, especially in literary domains where relationship extraction requires nuanced understanding but lacks annotations. this section details the creation of artifi-cial relationships in fiction through three stages: source selection, chunking, and synthetic relationship generation. the dataset foot_0 is available in three configurations, each supporting distinct analytical needs in literary nlp. ## dataset statistics, evaluation, and analysis this section presents an overview of the dataset, including key statistics, examples of extracted relations, and a deeper analysis of its structure. the insights provided here inform potential research directions in nlp applications for fiction. ## use cases this dataset offers valuable applications in fictionspecific nlp. it enables model finetuning, helping adapt nlp models for relationship extraction in literary narratives. it also supports literary analysis, allowing researchers to study character networks, relationship evolution, and thematic trends at scale. additionally, it has creative applications, enhancing ai-driven storytelling and character development for writers, game designers, and digital creators by ensuring richer, more consistent narratives. ## limitations & future work while this dataset advances fiction-specific re, its synthetic nature pose challenges. below, we outline key limitations and propose future directions to address them. gpt-4o-generated annotations may introduce biases or inaccuracies, especially for complex or implicit relationships requiring deeper narrative understanding. the reliance on five-sentence chunks, while computationally feasible, limits the capture of long-range relationships across chapters or books, and the absence of explicit coreference resolution hinders tracking evolving character interactions. without systematic human validation, precision and recall remain unverified, highlighting the need for manual evaluation. future work includes a small-scale human validation study, leveraging openai's structured output mode for stricter ontology adherence, and integrating coreference resolution to improve continuity. adaptive chunking strategies may enhance long-range dependency extraction. comparative studies with other models and relation extraction systems will assess performance, while active learning could expand the dataset efficiently. addressing these limitations will enhance reliability and broaden applicability in literary nlp research, enabling deeper narrative analysis. ## conclusion this paper introduces artificial relationships in fiction, a synthetically annotated dataset for relation extraction in literary texts. built from publicdomain fiction and gpt-4o generated relationships, arf bridges structured re tasks and fictional narratives. our analysis demonstrates its ability to capture diverse literary relationships, supporting research in character networks, thematic links, and narrative nlp. challenges include synthetic biases and scalability, requiring future work on human validation and dataset expansion. we envision arf as a foundational resource for nlp, literary analysis, and ai-driven storytelling.