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Wheel_of_Fortune_Questions

Wheel_of_Fortune_Questions

in_preparation ARFF Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) Visibility: public Uploaded 30-06-2024 by Iwo Godzwon
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Description: The 'wheel_of_fortune.csv' dataset is an intriguing collection designed for various applications, including natural language processing, game development, and cultural studies. It encapsulates the essence of categories, words, and puzzles typical to the 'Wheel of Fortune' style games or lexical analysis tasks. Attribute Description: 1. Category: This attribute details the theme of the 'Word to Guess'. It consists of categorical strings such as 'Fictional Place,' 'In The Kitchen,' 'The 90s,' 'Place,' and 'Song Lyrics'. These categories offer insights into the diverse types of prompts used in the game or study. 2. Word to Guess: This is the phrase or word participants aim to guess. Sample values include 'Gas Grill,' 'Kitchen Island,' 'Metal Wine Rack,' 'Worn Rug,' and 'Backyard Bird Feeder'. The variety reflects different levels of complexity and breadth of knowledge required. 3. Number of Words: Numerical data indicating how many words comprise the 'Word to Guess.' Examples vary from 2 to 4, showing different puzzle lengths. 4. Total Number of Letters: Illustrates the length of the 'Word to Guess' in letters, ranging from 9 to 22. It provides a heuristic measure of the challenge's difficulty. 5. First Word Letters: This numerical value represents the letter count of the first word in the 'Word to Guess,' with samples from 4 to 10. It offers initial insight or a starting point for solving the puzzle. Use Case: This dataset is invaluable for developers aiming to create lexical games or puzzles, educational technologists designing language learning apps, and cultural researchers studying the evolution of language games. By analyzing the dataset, one can develop algorithms for automated puzzle generation, study cultural trends in word usage, or enhance language learning methodologies through gamification. It's also suitable for challenges in machine learning, specifically in natural language processing (NLP), where understanding patterns and complexities in language games can contribute to advancements in AI linguistic capabilities.

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