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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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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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+
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+ ## Dataset Details
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+
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+ ### Dataset Description
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+
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+ - **Language(s):** English
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+ - **License:** Apache-2.0
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+
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+ ### Dataset Sources
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+
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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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+
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+ ## Uses
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+
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+ ### Direct Use
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+
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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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+
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+ ### Out-of-Scope Use
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+
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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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+
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+ ## Dataset Structure
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+
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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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+
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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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+
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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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+
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+ ## Dataset Creation
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+
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+ ### Curation Rationale
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+
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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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+
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+ ### Source Data
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+
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+ #### Data Collection and Processing
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+
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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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+
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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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+
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+ #### Who are the source data producers?
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+
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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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+
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+ ## Bias, Risks, and Limitations
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+
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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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+
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+
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+ ## Citation
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+
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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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+ ```