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README.md
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license: apache-2.0
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---
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license: apache-2.0
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task_categories:
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- text-classification
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- summarization
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language:
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- en
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tags:
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- named-entity-recognition
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- synthetic-data
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---
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# Dataset Card for WHODUNIT: Evaluation Benchmark for Culprit Detection in Mystery Stories
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This dataset contains crime and mystery novels along with their metadata. Each entry includes the full text, title, author, book length, and a list of identified culprits. Additionally, an augmented version of the dataset introduces entity replacements and synthetic data variations.
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## Dataset Details
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### Dataset Description
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- **Language(s):** English
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- **License:** Apache-2.0
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### Dataset Sources
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- **Repository:** [WhoDunIt Evaluation Benchmark](https://github.com/kjgpta/WhoDunIt-Evaluation_benchmark_for_culprit_detection_in_mystery_stories)
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## Uses
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### Direct Use
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This dataset can be used for:
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- Training models for text classification based on authorship, themes, or book characteristics.
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- Named Entity Recognition (NER) for detecting culprits and other entities in crime stories.
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- Summarization tasks for generating concise descriptions of mystery novels.
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- Text generation and storytelling applications.
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- Evaluating models' robustness against entity alterations using the augmented dataset.
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### Out-of-Scope Use
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- The dataset should not be used for real-world criminal investigations or forensic profiling.
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- Any misuse involving biased predictions or unethical AI applications should be avoided.
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## Dataset Structure
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### Data Fields
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#### **Original Dataset**
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- `text` (*string*): The full text or an excerpt from the novel.
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- `title` (*string*): The title of the novel.
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- `author` (*string*): The author of the novel.
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- `length` (*integer*): The number of pages in the novel.
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- `culprit_ids` (*list of strings*): The list of culprits in the story.
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#### **Augmented Dataset**
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- Contains the same fields as the original dataset.
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- Additional field:
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- `metadata` (*dict*): Information on entity replacement strategies (e.g., replacing names with fictional or thematic counterparts).
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- Modified `culprit_ids`: The culprits' names have been replaced using different replacement styles (e.g., random names, thematic names, etc.).
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### Data Splits
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Both the original and augmented datasets are provided as single corpora without predefined splits.
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## Dataset Creation
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### Curation Rationale
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This dataset was curated to aid in the study of crime fiction narratives and their structural patterns, with a focus on culprit detection in mystery stories. The augmented dataset was created to test the robustness of NLP models against entity modifications.
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### Source Data
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#### Data Collection and Processing
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The original dataset is curated from public domain literary works. The text is processed to extract relevant metadata such as title, author, book length, and named culprits.
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The augmented dataset introduces variations using entity replacement techniques, where character names are substituted based on predefined rules (e.g., random names, theme-based replacements, etc.).
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#### Who are the source data producers?
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The dataset is composed of classic crime and mystery novels written by renowned authors such as Agatha Christie, Arthur Conan Doyle, and Fyodor Dostoevsky.
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## Bias, Risks, and Limitations
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- The dataset consists primarily of classic literature, which may not reflect modern storytelling techniques.
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- The augmented dataset's entity replacements may introduce artificial biases.
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- It may have inherent biases based on the cultural and historical context of the original works.
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## Citation
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**BibTeX:**
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```
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@misc{gupta2025whodunitevaluationbenchmarkculprit,
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title={WHODUNIT: Evaluation benchmark for culprit detection in mystery stories},
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author={Kshitij Gupta},
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year={2025},
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eprint={2502.07747},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2502.07747},
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}
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```
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