Enhancing user creativity: Semantic measures for idea generation
- URL: http://arxiv.org/abs/2106.10131v1
- Date: Fri, 18 Jun 2021 13:47:56 GMT
- Title: Enhancing user creativity: Semantic measures for idea generation
- Authors: Georgi V. Georgiev, Danko D. Georgiev
- Abstract summary: We analyze a dataset of design problem-solving conversations in real-world settings by using 49 semantic measures based on WordNet 3.1.
We show that a divergence of semantic similarity, an increased information content, and a decreased polysemy predict the success of generated ideas.
These results advance cognitive science by identifying real-world processes in human problem solving.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Human creativity generates novel ideas to solve real-world problems. This
thereby grants us the power to transform the surrounding world and extend our
human attributes beyond what is currently possible. Creative ideas are not just
new and unexpected, but are also successful in providing solutions that are
useful, efficient and valuable. Thus, creativity optimizes the use of available
resources and increases wealth. The origin of human creativity, however, is
poorly understood, and semantic measures that could predict the success of
generated ideas are currently unknown. Here, we analyze a dataset of design
problem-solving conversations in real-world settings by using 49 semantic
measures based on WordNet 3.1 and demonstrate that a divergence of semantic
similarity, an increased information content, and a decreased polysemy predict
the success of generated ideas. The first feedback from clients also enhances
information content and leads to a divergence of successful ideas in creative
problem solving. These results advance cognitive science by identifying
real-world processes in human problem solving that are relevant to the success
of produced solutions and provide tools for real-time monitoring of problem
solving, student training and skill acquisition. A selected subset of
information content (IC S\'anchez-Batet) and semantic similarity
(Lin/S\'anchez-Batet) measures, which are both statistically powerful and
computationally fast, could support the development of technologies for
computer-assisted enhancements of human creativity or for the implementation of
creativity in machines endowed with general artificial intelligence.
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