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

Author

Listed:
  • Benjamin M. Knisely

    (United States Army Medical Research and Development Command)

  • Holly H. Pavliscsak

    (United States Army Medical Research and Development Command)

Abstract

Funding institutions often solicit text-based research proposals to evaluate potential recipients. Leveraging the information contained in these documents could help institutions understand the supply of research within their domain. In this work, an end-to-end methodology for semi-supervised document clustering is introduced to partially automate classification of research proposals based on thematic areas of interest. The methodology consists of three stages: (1) manual annotation of a document sample; (2) semi-supervised clustering of documents; (3) evaluation of cluster results using quantitative metrics and qualitative ratings (coherence, relevance, distinctiveness) by experts. The methodology is described in detail to encourage replication and is demonstrated on a real-world data set. This demonstration sought to categorize proposals submitted to the US Army Telemedicine and Advanced Technology Research Center (TATRC) related to technological innovations in military medicine. A comparative analysis of method features was performed, including unsupervised vs. semi-supervised clustering, several document vectorization techniques, and several cluster result selection strategies. Outcomes suggest that pretrained Bidirectional Encoder Representations from Transformers (BERT) embeddings were better suited for the task than older text embedding techniques. When comparing expert ratings between algorithms, semi-supervised clustering produced coherence ratings ~ 25% better on average compared to standard unsupervised clustering with negligible differences in cluster distinctiveness. Last, it was shown that a cluster result selection strategy that balances internal and external validity produced ideal results. With further refinement, this methodological framework shows promise as a useful analytical tool for institutions to unlock hidden insights from untapped archives and similar administrative document repositories.

Suggested Citation

  • Benjamin M. Knisely & Holly H. Pavliscsak, 2023. "Research proposal content extraction using natural language processing and semi-supervised clustering: A demonstration and comparative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 3197-3224, May.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:5:d:10.1007_s11192-023-04689-3
    DOI: 10.1007/s11192-023-04689-3
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    References listed on IDEAS

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    1. Leah G. Nichols, 2014. "A topic model approach to measuring interdisciplinarity at the National Science Foundation," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 741-754, September.
    2. Zhang, Yi & Lu, Jie & Liu, Feng & Liu, Qian & Porter, Alan & Chen, Hongshu & Zhang, Guangquan, 2018. "Does deep learning help topic extraction? A kernel k-means clustering method with word embedding," Journal of Informetrics, Elsevier, vol. 12(4), pages 1099-1117.
    3. Brock, Guy & Pihur, Vasyl & Datta, Susmita & Datta, Somnath, 2008. "clValid: An R Package for Cluster Validation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i04).
    4. Jaeyoung Kim & Janghyeok Yoon & Eunjeong Park & Sungchul Choi, 2020. "Patent document clustering with deep embeddings," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 563-577, May.
    5. Jakob Edler & Wouter P Boon, 2018. "‘The next generation of innovation policy: Directionality and the role of demand-oriented instruments’—Introduction to the special section," Science and Public Policy, Oxford University Press, vol. 45(4), pages 433-434.
    6. Yuan Zhou & Heng Lin & Yufei Liu & Wei Ding, 2019. "A novel method to identify emerging technologies using a semi-supervised topic clustering model: a case of 3D printing industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 167-185, July.
    7. Kevin W. Boyack & Caleb Smith & Richard Klavans, 2018. "Toward predicting research proposal success," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 449-461, February.
    8. Ebadi, Ashkan & Tremblay, Stéphane & Goutte, Cyril & Schiffauerova, Andrea, 2020. "Application of machine learning techniques to assess the trends and alignment of the funded research output," Journal of Informetrics, Elsevier, vol. 14(2).
    Full references (including those not matched with items on IDEAS)

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