{"url":"/dataset/arf","name":"ARF","full_name":"Artificial Relationships in Fiction","description_markdown":"# Artificial Relationships in Fiction\r\n\r\n## Dataset Description\r\nArtificial 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.\r\n\r\n## Dataset Configurations and Features\r\n\r\n### Configurations\r\n- `fiction_books`: Metadata-rich corpus of 6,322 public domain fiction books (1850–1950) with inferred author gender and thematic categorization.\r\n- `fiction_books_in_chunks`: Books segmented into 5-sentence chunks (5.96M total), preserving narrative coherence via 1-sentence overlap.\r\n- `fiction_books_with_relations`: A subset of 95,475 text chunks annotated with 128,000+ relationships using GPT-4o and a fiction-specific ontology.\r\n\r\n\r\n### 1. Configuration: `fiction_books`\r\n- **Description**: Contains the full text and metadata of 6,322 English-language fiction books from Project Gutenberg.\r\n- **Features**:\r\n  - `book_id`: Unique Project Gutenberg ID.\r\n  - `title`: Title of the book.\r\n  - `author`: Author name.\r\n  - `author_birth_year` / `author_death_year`: Author lifespan.\r\n  - `release_date`: PG release date.\r\n  - `subjects`: List of thematic topics (mapped to 51 standardized themes).\r\n  - `gender`: Inferred author gender (via GPT-4o).\r\n  - `text`: Cleaned full book text.\r\n- **Use Case**: Supports thematic and demographic analysis of literary texts.\r\n\r\n### 2. Configuration: `fiction_books_in_chunks`\r\n- **Description**: Each book is segmented into overlapping five-sentence text chunks to enable granular NLP analysis.\r\n- **Features**:\r\n  - `book_id`, `chunk_index`: Book and chunk identifiers.\r\n  - `text_chunk`: Five-sentence excerpt from the book.\r\n- **Use Case**: Facilitates sequence-level tasks like coreference resolution or narrative progression modeling.\r\n\r\n### 3. Configuration: `synthetic_relations_in_fiction_books` (ARF)\r\n- **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\"*.\r\n- **Features**:\r\n  - `book_id`, `chunk_index`: Identifiers.\r\n  - `text_chunk`: Five-sentence text segment.\r\n  - `relations`: A list of structured relation annotations, each containing:\r\n    - `entity1`, `entity2`: Text spans.\r\n    - `entity1Type`, `entity2Type`: Entity types based on ontology.\r\n    - `relation`: Relationship type.\r\n- **Use Case**: Ideal for training and evaluating RE models in fictional narratives, studying character networks, and generating structured data from literary texts.\r\n\r\n\r\n\r\n## ARF Dataset Structure (config 'synthetic_relations_in_fiction_books')\r\nEach annotated relation is formatted as:\r\n```json\r\n{\r\n  \"entity1\": \"Head Entity text\",\r\n  \"entity2\": \"Tail Entity text\",\r\n  \"entity1Type\": \"Head entity type\",\r\n  \"entity2Type\": \"Tail entity type\",\r\n  \"relation\": \"Relation type\"\r\n}\r\n```\r\n\r\nExample:\r\n```json\r\n{\r\n  \"entity1\": \"Vortigern\",\r\n  \"entity2\": \"castle\",\r\n  \"entity1Type\": \"PER\",\r\n  \"entity2Type\": \"FAC\",\r\n  \"relation\": \"owns\"\r\n}\r\n```\r\n\r\n### Entity Types (11)\r\n| Entity Type | Description |\r\n|-------------|-------------|\r\n| `PER` | Person or group of people |\r\n| `FAC` | Facility – man-made structures for human use |\r\n| `LOC` | Location – natural or loosely defined geographic regions |\r\n| `WTHR` | Weather – atmospheric or celestial phenomena |\r\n| `VEH` | Vehicle – transport devices (e.g., ship, carriage) |\r\n| `ORG` | Organization – formal groups or institutions |\r\n| `EVNT` | Event – significant occurrences in narrative |\r\n| `TIME` | Time – chronological or historical expressions |\r\n| `OBJ` | Object – tangible items in the text |\r\n| `SENT` | Sentiment – emotional states or feelings |\r\n| `CNCP` | Concept – abstract ideas or motifs |\r\n\r\n### Relation Types (48)\r\n| Relation Type        | Entity 1 Type     | Entity 2 Type     | Description                               |\r\n|----------------------|------------------|-------------------|-------------------------------------------|\r\n| `parent_father_of` | `PER` | `PER` | Father relationship |\r\n| `parent_mother_of` | `PER` | `PER` | Mother relationship |\r\n| `child_of` | `PER` | `PER` | Child to parent |\r\n| `sibling_of` | `PER` | `PER` | Sibling relationship |\r\n| `spouse_of` | `PER` | `PER` | Spousal relationship |\r\n| `relative_of` | `PER` | `PER` | Extended family relationship |\r\n| `adopted_by` | `PER` | `PER` | Adopted by another person |\r\n| `companion_of` | `PER` | `PER` | Companionship or ally |\r\n| `friend_of` | `PER` | `PER` | Friendship |\r\n| `lover_of` | `PER` | `PER` | Romantic relationship |\r\n| `rival_of` | `PER` | `PER` | Rivalry |\r\n| `enemy_of` | `PER/ORG` | `PER/ORG` | Hostile or antagonistic relationship |\r\n| `inspires` | `PER` | `PER` | Inspires or motivates |\r\n| `sacrifices_for` | `PER` | `PER` | Makes a sacrifice for |\r\n| `mentor_of` | `PER` | `PER` | Mentorship or guidance |\r\n| `teacher_of` | `PER` | `PER` | Formal teaching relationship |\r\n| `protector_of` | `PER` | `PER` | Provides protection to |\r\n| `employer_of` | `PER` | `PER` | Employment relationship |\r\n| `leader_of` | `PER` | `ORG` | Leader of an organization |\r\n| `member_of` | `PER` | `ORG` | Membership in an organization |\r\n| `lives_in` | `PER` | `FAC/LOC` | Lives in a location |\r\n| `lived_in` | `PER` | `TIME` | Historically lived in |\r\n| `visits` | `PER` | `FAC` | Visits a facility |\r\n| `travel_to` | `PER` | `LOC` | Travels to a location |\r\n| `born_in` | `PER` | `LOC` | Birthplace |\r\n| `travels_by` | `PER` | `VEH` | Travels by a vehicle |\r\n| `participates_in` | `PER` | `EVNT` | Participates in an event |\r\n| `causes` | `PER` | `EVNT` | Causes an event |\r\n| `owns` | `PER` | `OBJ` | Owns an object |\r\n| `believes_in` | `PER` | `CNCP` | Believes in a concept |\r\n| `embodies` | `PER` | `CNCP` | Embodies a concept |\r\n| `located_in` | `FAC` | `LOC` | Located in a place |\r\n| `part_of` | `FAC/LOC/ORG` | `FAC/LOC/ORG` | Part of a larger entity |\r\n| `owned_by` | `FAC/VEH` | `PER` | Owned by someone |\r\n| `occupied_by` | `FAC` | `PER` | Occupied by someone |\r\n| `used_by` | `FAC` | `ORG` | Used by an organization |\r\n| `affects` | `WTHR` | `LOC/EVNT` | Weather affects location or event |\r\n| `experienced_by` | `WTHR` | `PER` | Weather experienced by someone |\r\n| `travels_in` | `VEH` | `LOC` | Vehicle travels in a location |\r\n| `based_in` | `ORG` | `LOC` | Organization based in a location |\r\n| `attended_by` | `EVNT` | `PER` | Event attended by person |\r\n| `ends_in` | `EVNT` | `TIME` | Event ends at a time |\r\n| `occurs_in` | `EVNT` | `LOC/TIME` | Event occurs in a place or time |\r\n| `features` | `EVNT` | `OBJ` | Event features an object |\r\n| `stored_in` | `OBJ` | `LOC/FAC` | Object stored in a place |\r\n| `expressed_by` | `SENT` | `PER` | Sentiment expressed by person |\r\n| `used_by` | `OBJ` | `PER` | Object used by person |\r\n| `associated_with` | `CNCP` | `EVNT` | Concept associated with event |\r\n\r\n## Dataset Statistics\r\n| Metric                     | Value      |\r\n|----------------------------|------------|\r\n| Books                     | 96         |\r\n| Authors                   | 91         |\r\n| Gender Ratio (M/F)        | 55% / 45%  |\r\n| Subgenres                 | 51         |\r\n| Annotated Chunks          | 95,475     |\r\n| Relations per Chunk       | 1.34 avg   |\r\n| Chunks with No Relations  | 35,230     |\r\n| Total Relations           | ~128,000   |\r\n\r\n## Methodology\r\n- **Source Texts**: English-language fiction from PG bookshelves: Fiction, Children & YA, Crime/Mystery.\r\n- **Annotation Model**: GPT-4o via custom prompt integrating strict ontologies.\r\n- **Sampling**: Balanced author gender and thematic distributions.\r\n- **Ontology Adherence**: <0.05% deviation for entities; 2.95% for relations.\r\n- **Format**: Structured JSON, optimized for NLP pipelines.\r\n\r\n## Applications\r\n- **Fine-tuning RE Models**: Adapt models to literary domains with implicit, evolving relationships.\r\n- **Computational Literary Studies**: Analyze character networks, thematic evolution, and genre patterns.\r\n- **Creative AI**: Enhance AI-driven storytelling, character consistency, and world-building tools.\r\n\r\n## Citation\r\nIf you use this dataset in your research, please cite:\r\n\r\n```bibtex\r\n@inproceedings{christou-tsoumakas-2025-artificial,\r\n    title = \"Artificial Relationships in Fiction: A Dataset for Advancing {NLP} in Literary Domains\",\r\n    author = \"Christou, Despina  and Tsoumakas, Grigorios\",\r\n    editor = \"Kazantseva, Anna and Szpakowicz, Stan and Degaetano-Ortlieb, Stefania and Bizzoni, Yuri and Pagel, Janis\",\r\n    booktitle = \"Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025)\",\r\n    month = may,\r\n    year = \"2025\",\r\n    address = \"Albuquerque, New Mexico\",\r\n    publisher = \"Association for Computational Linguistics\",\r\n    url = \"https://aclanthology.org/2025.latechclfl-1.13/\",\r\n    pages = \"130--147\",\r\n    ISBN = \"979-8-89176-241-1\"\r\n}\r\n```","description_withheld":null,"homepage":"https://huggingface.co/datasets/Despina/project_gutenberg","introduced_date":"2025-05-04","introduced_date_note":null,"introduced_by":null,"license":{"name":"MIT","url":null},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Relation Extraction","url":"/task/relation-extraction","datasets_with_task":"/datasets/task/relation-extraction"},{"name":"Named Entity Recognition","url":"/task/named-entity-recognition-1","datasets_with_task":"/datasets/task/named-entity-recognition-1"},{"name":"Key Information Extraction","url":"/task/key-information-extraction","datasets_with_task":"/datasets/task/key-information-extraction"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["ARF"],"data_loaders":[],"num_papers_in_archive":0,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}