用文本挖掘分析提问方式如何影响创造力，揭示问题类型与复杂度的作用。
Applying Text Mining to Analyze Human Question Asking in Creativity Research
- 通过文本挖掘分析问题类型、复杂度及答案内容，评估提问的创造性潜力。
- 在五个数据集上验证方法，发现提问特征与创意生成显著相关。
- 适合对认知科学、人机共创和NLP应用感兴趣的学者参考。
创造力关乎在特定领域生成新颖且有效的想法。这些创意是如何产生的？一种日益受到实证关注的认知机制是提问。提问可能通过界定问题、促进创造性解题来支持创意构思。然而，关于提问在创造力中的确切作用仍知之甚少。本文尝试运用文本挖掘方法衡量提问的认知潜力，综合考虑（a）问题类型、（b）问题复杂度以及（c）答案内容。该研究总结了问题挖掘在创造力研究中的发展脉络，并介绍了适用于该领域的自然语言处理方法。此外，提出并实现了一种新方法，应用于五个数据集。实验结果经过全面分析，表明自然语言处理在创造性研究中具有实际价值。
原文摘要 · Abstract (English)
Creativity relates to the ability to generate novel and effective ideas in the areas of interest. How are such creative ideas generated? One possible mechanism that supports creative ideation and is gaining increased empirical attention is by asking questions. Question asking is a likely cognitive mechanism that allows defining problems, facilitating creative problem solving. However, much is unknown about the exact role of questions in creativity. This work presents an attempt to apply text mining methods to measure the cognitive potential of questions, taking into account, among others, (a) question type, (b) question complexity, and (c) the content of the answer. This contribution summarizes the history of question mining as a part of creativity research, along with the natural language processing methods deemed useful or helpful in the study. In addition, a novel approach is proposed, implemented, and applied to five datasets. The experimental results obtained are comprehensively analyzed, suggesting that natural language processing has a role to play in creative research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。