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

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

SUBMITTER: Knisely BM 

PROVIDER: S-EPMC10083066 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature