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Using Artificial Intuition in Distinct, Minimalist Classification of Scientific Abstracts for Management of Technology Portfolios

arXiv 2508.13182 ↗First posted 13 Aug 2025cs.DL cs.AI cs.LGcited by 0

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Abstract (arXiv)

Classification of scientific abstracts is useful for strategic activities but challenging to automate because the sparse text provides few contextual clues. Metadata associated with the scientific publication can be used to improve performance but still often requires a semi-supervised setting. Moreover, such schemes may generate labels that lack distinction -- namely, they overlap and thus do not uniquely define the abstract. In contrast, experts label and sort these texts with ease. Here we describe an application of a process we call artificial intuition to replicate the expert's approach, using a Large Language Model (LLM) to generate metadata. We use publicly available abstracts from the United States National Science Foundation to create a set of labels, and then we test this on a set of abstracts from the Chinese National Natural Science Foundation to examine funding trends. We demonstrate the feasibility of this method for research portfolio management, technology scouting, and other strategic activities.

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References · 9

  1. 2024TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervisioncited by 25
  2. 2023Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understandingcited by 16
  3. 2023Chain-of-Factors Paper-Reviewer Matchingcited by 11
  4. 2025Scientific Paper Retrieval with LLM-Guided Semantic-Based Rankingcited by 10
  5. 2024Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classificationcited by 7
  6. 2025CoRank: LLM-Based Compact Reranking with Document Features for Scientific Retrievalcited by 5
  7. 2024Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Textscited by 3
  8. 2024Artificial Intuition: Efficient Classification of Scientific Abstractscited by 2
  9. 2024Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMscited by 1

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