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Research proposal content extraction using natural language processing and semi-supervised clustering: A

Benjamin M Knisely1, Holly H Pavliscsak1

  • 1Telemedicine and Advanced Technology Research Center, United States Army Medical Research and Development Command, Fort Detrick, MD 21702 USA.

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Summary
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This study introduces a semi-supervised clustering method to automatically classify research proposals by theme. The approach enhances thematic analysis for funding institutions, improving understanding of research landscapes.

Keywords:
Cluster validationDocument clusteringMachine learningResearch portfolioText mining

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Area of Science:

  • Information Science
  • Computational Linguistics
  • Data Science

Background:

  • Funding institutions receive numerous research proposals for evaluation.
  • Analyzing these proposals manually is time-consuming and resource-intensive.
  • Understanding research supply and thematic distribution is crucial for strategic funding.

Purpose of the Study:

  • To develop and evaluate an end-to-end methodology for semi-supervised document clustering.
  • To automate the classification of research proposals into thematic areas.
  • To assess the effectiveness of different clustering techniques and vectorization methods.

Main Methods:

  • A three-stage methodology: manual annotation, semi-supervised clustering, and expert evaluation.
  • Utilized Bidirectional Encoder Representations from Transformers (BERT) for document vectorization.
  • Compared unsupervised versus semi-supervised clustering and various selection strategies.

Main Results:

  • Semi-supervised clustering achieved approximately 25% higher coherence ratings than unsupervised clustering.
  • Pretrained BERT embeddings outperformed older text embedding techniques.
  • A balanced cluster selection strategy optimized results.

Conclusions:

  • The proposed semi-supervised clustering framework effectively categorizes research proposals.
  • This methodology offers a promising tool for institutions to analyze administrative documents.
  • It facilitates unlocking insights from untapped research archives.