ARF (Artificial Relationships in Fiction)

Artificial Relationships in Fiction

Dataset Description

Artificial Relationships in Fiction (ARF) is a synthetically annotated dataset for Relation Extraction (RE) in fiction, created from a curated selection of literary texts sourced from Project Gutenberg. The dataset captures the rich, implicit relationships within fictional narratives using a novel ontology and GPT-4o for annotation. ARF is the first large-scale RE resource designed specifically for literary texts, advancing both NLP model training and computational literary analysis.

Dataset Configurations and Features

Configurations

• fiction_books: Metadata-rich corpus of 6,322 public domain fiction books (1850–1950) with inferred author gender and thematic categorization.
• fiction_books_in_chunks: Books segmented into 5-sentence chunks (5.96M total), preserving narrative coherence via 1-sentence overlap.
• fiction_books_with_relations: A subset of 95,475 text chunks annotated with 128,000+ relationships using GPT-4o and a fiction-specific ontology.

1. Configuration: fiction_books

• Description: Contains the full text and metadata of 6,322 English-language fiction books from Project Gutenberg.
• Features:
• book_id: Unique Project Gutenberg ID.
• title: Title of the book.
• author: Author name.
• author_birth_year / author_death_year: Author lifespan.
• release_date: PG release date.
• subjects: List of thematic topics (mapped to 51 standardized themes).
• gender: Inferred author gender (via GPT-4o).
• text: Cleaned full book text.
• Use Case: Supports thematic and demographic analysis of literary texts.

2. Configuration: fiction_books_in_chunks

• Description: Each book is segmented into overlapping five-sentence text chunks to enable granular NLP analysis.
• Features:
• book_id, chunk_index: Book and chunk identifiers.
• text_chunk: Five-sentence excerpt from the book.
• Use Case: Facilitates sequence-level tasks like coreference resolution or narrative progression modeling.

3. Configuration: synthetic_relations_in_fiction_books (ARF)

• Description: This subset corresponds to the Artificial Relationships in Fiction (ARF) dataset proposed in the LaTeCH-CLfL 2025 paper "Artificial Relationships in Fiction: A Dataset for Advancing NLP in Literary Domains".
• Features:
• book_id, chunk_index: Identifiers.
• text_chunk: Five-sentence text segment.
• relations: A list of structured relation annotations, each containing:
  • entity1, entity2: Text spans.
  • entity1Type, entity2Type: Entity types based on ontology.
  • relation: Relationship type.
• Use Case: Ideal for training and evaluating RE models in fictional narratives, studying character networks, and generating structured data from literary texts.

ARF Dataset Structure (config 'synthetic_relations_in_fiction_books')

Each annotated relation is formatted as:

{
  "entity1": "Head Entity text",
  "entity2": "Tail Entity text",
  "entity1Type": "Head entity type",
  "entity2Type": "Tail entity type",
  "relation": "Relation type"
}

Example:

{
  "entity1": "Vortigern",
  "entity2": "castle",
  "entity1Type": "PER",
  "entity2Type": "FAC",
  "relation": "owns"
}

Entity Types (11)

Relation Types (48)

Dataset Statistics

Methodology

• Source Texts: English-language fiction from PG bookshelves: Fiction, Children & YA, Crime/Mystery.
• Annotation Model: GPT-4o via custom prompt integrating strict ontologies.
• Sampling: Balanced author gender and thematic distributions.
• Ontology Adherence: <0.05% deviation for entities; 2.95% for relations.
• Format: Structured JSON, optimized for NLP pipelines.

Applications

• Fine-tuning RE Models: Adapt models to literary domains with implicit, evolving relationships.
• Computational Literary Studies: Analyze character networks, thematic evolution, and genre patterns.
• Creative AI: Enhance AI-driven storytelling, character consistency, and world-building tools.

Citation

If you use this dataset in your research, please cite:

@inproceedings{christou-tsoumakas-2025-artificial,
    title = "Artificial Relationships in Fiction: A Dataset for Advancing {NLP} in Literary Domains",
    author = "Christou, Despina  and Tsoumakas, Grigorios",
    editor = "Kazantseva, Anna and Szpakowicz, Stan and Degaetano-Ortlieb, Stefania and Bizzoni, Yuri and Pagel, Janis",
    booktitle = "Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.latechclfl-1.13/",
    pages = "130--147",
    ISBN = "979-8-89176-241-1"
}