Enhancing user creativity: Semantic measures for idea generation (2106.10131v1)

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.

Okay, here's a detailed summary of the paper "Enhancing User Creativity: Semantic Measures for Idea Generation" by Georgi V. Georgiev and Danko D. Georgiev.

Core Idea and Goal:

The paper's central premise is that human creativity, specifically in the context of design problem-solving, can be quantified and predicted using semantic analysis of language. The authors hypothesize that the way people talk about ideas (specifically, the nouns they use) reveals underlying cognitive processes related to convergence and divergence, and that these processes correlate with the success or failure of those ideas. The ultimate goal is to develop tools that can, in real-time, assess the potential of an idea based on its semantic properties and, potentially, guide users towards more successful creative outcomes. This has implications both for understanding human creativity and for building AI systems that can either enhance human creativity or exhibit creativity themselves.

Key Concepts and Definitions:

• Creativity: Defined as the ability to generate novel, unexpected, and useful/valuable solutions to problems. It's not just about being new; the ideas must also be effective.
• Convergent Thinking: Analytical thinking that focuses on finding a single, correct answer or solution. It tends to refine existing ideas and identify relationships already believed to exist.
• Divergent Thinking: Associative thinking that explores multiple possibilities and seeks connections between seemingly unrelated concepts. It's about generating new ideas.
• Semantic Networks: Computational structures that represent the relationships between concepts (in this case, represented by nouns). Nodes are concepts, and links show semantic connections (like "is-a" relationships, e.g., "dog" is-a "mammal").
• WordNet 3.1: A large lexical database of English that organizes words into sets of synonyms (synsets) and defines relationships between them. It's the foundation for the semantic analysis in this paper. Think of it as a giant, structured dictionary.
• Semantic Similarity: A measure of how closely related two words (nouns) are in meaning, based on their positions and connections within WordNet. There are various ways to calculate this, as detailed in the paper.
• Information Content (IC): A measure of how specific or informative a word is. A word with many hyponyms (more specific terms under it) has higher IC. Several different formulas for calculating IC are used and compared.
• Polysemy: The number of different meanings a word has. High polysemy means a word is ambiguous.
• Level of Abstraction: How general or specific a word is. "Entity" is highly abstract; a specific type of dog breed is very concrete (low abstraction).
• Idea (in this context): Defined as the formulation of the design. For example, sketches, principles of actions and target group.
• Successful Idea: One developed to full completion.
• Unsuccessful Idea: One not fully developed or disregarded.

Methodology:

1. Data Source: The researchers used transcripts of "design review conversations" from industrial design students at Purdue University. These conversations involved students, instructors, and real clients discussing design problems and solutions.
2. Data Preprocessing:
   • Transcripts were cleaned (removing non-verbal cues, timestamps, etc.).
   • NLP techniques (using NLTK and TextBlob) were used to identify and extract nouns.
   • Plural nouns were converted to singular, and nouns not found in WordNet were removed (a very small percentage).
3. Semantic Network Construction: For each segment of conversation (e.g., related to a specific idea, or before/after feedback), a semantic network was built using the extracted nouns and their relationships as defined in WordNet 3.1.
4. Semantic Measure Calculation: A large number of semantic measures (49 in total) were calculated for each network:
   • Level of Abstraction: (1 measure)
   • Polysemy: (1 measure)
   • Information Content (IC): (7 different formulas)
   • Semantic Similarity: (40 different formulas – 5 path-based, and 5 IC-based, with the latter combined with each of the 7 IC formulas). The formulas are detailed mathematically in the paper.
5. Time Dynamics Analysis: Crucially, the researchers looked at how these semantic measures changed over time within the conversations. They divided conversations into segments (e.g., 3 time points) and used linear trend lines to assess whether a measure was increasing (converging) or decreasing (diverging).
6. Statistical Analysis: Repeated-measures ANOVAs were used to compare the semantic measures across different conditions (e.g., student vs. instructor speech, successful vs. unsuccessful ideas, before/after feedback, before/after evaluation). Correlation analysis and hierarchical clustering were used to examine the relationships between the different semantic similarity and IC measures.

Key Findings:

1. Student and Instructor Thinking: Surprisingly, there were no significant differences in the semantic measures between student and instructor speech. This suggests that both parties were engaged in similar cognitive processes during the discussions.
2. Divergence Predicts Success: This is the core finding. Conversations about successful ideas showed a decrease in semantic similarity over time (divergence), while conversations about unsuccessful ideas showed an increase in semantic similarity (convergence). This supports the idea that divergent thinking is crucial for generating successful creative solutions.
3. Information Content Increases: Successful ideas also tended to show an increase in information content over time, and a decrease in polysemy, suggesting that successful ideas become more specific and less ambiguous.
4. IC-Based Similarity Measures are Best: The 40 different semantic similarity measures were not all equal. The purely IC-based measures (especially those using the Lin, Jiang-Conrath, and Resnik formulas) performed best at distinguishing between successful and unsuccessful ideas. Path-based measures were less effective. Among the IC formulas, Sanchez-Batet, Blanchard, and Seco were the top performers.
5. First Feedback Matters: After receiving the first feedback from the client, successful ideas showed even stronger divergence in semantic similarity and a further increase in information content. This highlights the importance of external critique in the creative process.
6. First Evaluation Has Less Impact: The first evaluation (by the instructor or client) had a much smaller effect on the semantic measures of successful ideas. This suggests that the evaluation primarily served to filter out unsuccessful ideas, rather than fundamentally changing the successful ones.

Implications and Discussion:

• Cognitive Science: The findings support theories that link creativity to divergent thinking and show that these cognitive processes can be objectively measured through language analysis. It reinforces the idea that both divergent (idea generation) and convergent (idea evaluation) processes are interwoven in creative problem-solving.
• Artificial Intelligence: The results have significant implications for AI research. The identified semantic measures (particularly the divergence of Lin/Sanchez-Batet semantic similarity) could be used to:
  • Enhance Human Creativity: Develop software that suggests ideas or transformations that are likely to be more divergent and successful.
  • Implement Creativity in AI: Build AI systems that can evaluate their own generated ideas and steer them towards more creative outcomes.
  • This could be used in many situations, including helping students in MOOCs.
• Future Work: The authors plan to:
  • Develop AI applications based on their findings (e.g., creativity-enhancing software, automated design education tools).
  • Validate their results with different datasets (e.g., conversations from professional design teams).
  • Explore whether semantic analysis can predict human behavior in other contexts (e.g., social media).

In essence, the paper provides a strong, data-driven argument that the language we use to discuss ideas reveals underlying cognitive processes related to creativity, and that these processes can be quantified and potentially harnessed to improve creative outcomes. It bridges cognitive science and AI research in a novel and impactful way.