Pilot: Computational Metaphor Identification for Supporting Creativity in Science Education

试点：支持科学教育创造力的计算隐喻识别

• 批准号: 0757646
• 负责人: William Tomlinson
• 金额: $ 20万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0757646&HistoricalAwards=false
• 关键词: Pilot; Computational; Metaphor; Identification; Supporting

An important aspect of creative thinking in science education is the set of metaphors that students use to understand and conceptualize material. Previous research has shown that metaphorical and analogical thinking are key components in creative processes and play an integral role in academic creativity. An important part of encouraging creativity through metaphor is raising a student's awareness of the conceptual metaphors they already use. However, identifying the metaphors that students use in their learning has previously been labor intensive, requiring a great deal of time and attention from a human instructor. Previous work in computational linguistics has enabled the automatic extraction of metaphor from bodies of written text. This project improves the state of the art in computational metaphor identification (CMI) through the use of large corpora, through the employment of typed dependency parsing, and through a comparison of several selectional preference learning techniques. The result will be a technological tool kit that supports human creativity by automatically identifying metaphors in bodies of text. This research makes a significant contribution to the fields of computer science and computational linguistics by improving existing metaphor extraction techniques through the integration of current research in selectional preference learning and dependency parsing. In the area of education research, the project will evaluate the impact of CMI on both students' thinking and teachers' teaching, as well as examining the perceived utility of CMI by both groups. This research will allow these metaphors to be used to anchor instruction. This project has the potential to improve creative thinking in science instruction by helping students and their teachers perceive more effectively the ways in which students are thinking about science concepts. In addition, the research may provide insight into how this type of system could enhance creativity and learning across a wide range of other disciplines. Finally, the ability to perform computational metaphor identification may have a variety of other potential applications, from automated search and information retrieval to knowledge representation and artificial intelligence.

科学教育中创造性思维的一个重要方面是学生用来理解和概念化材料的一组隐喻。以往的研究表明，隐喻和类比思维是创造性过程中的关键组成部分，并在学术创造力中发挥不可或缺的作用。通过隐喻鼓励创造力的一个重要部分是提高学生对他们已经使用的概念隐喻的意识。然而，识别学生在学习中使用的隐喻以前是劳动密集型的，需要人类教师的大量时间和注意力。在计算语言学的前期工作中，已经实现了从书面文本中自动提取隐喻。该项目通过使用大型语料库，通过使用类型依赖分析，并通过比较几种选择偏好学习技术，提高了计算隐喻识别（CMI）的最新水平。 其结果将是一个技术工具包，通过自动识别文本中的隐喻来支持人类的创造力。本研究通过整合选择偏好学习和依存句法分析的研究成果，改进了现有的隐喻提取技术，为计算机科学和计算语言学领域做出了重要贡献。在教育研究方面，研究计划会评估中文教学对学生思维和教师教学的影响，以及研究两组学生对中文教学的效用。这项研究将使这些隐喻被用来锚教学。该项目有可能通过帮助学生和他们的教师更有效地感知学生思考科学概念的方式来提高科学教学中的创造性思维。此外，这项研究还可以深入了解这种类型的系统如何在广泛的其他学科中提高创造力和学习能力。最后，执行计算隐喻识别的能力可能具有各种其他潜在的应用，从自动搜索和信息检索到知识表示和人工智能。