Enhancing General Language Models for Biomedical Test Retrieval via Diversified Prior Knowledge

The thesis introduces the Diversified Prior Knowledge Enhanced General Language Model (DPK-GLM) to improve the efficacy of general language models in biomedical Information Retrieval (IR). General language models often struggle with biomedical data due to its specialized terminology and the need for precise matching. DPK-GLM tackles these challenges by integrating domain-specific knowledge, thereby enhancing the model's ability to understand and process biomedical information. The framework comprises three core components. The first, Knowledge-based Query Expansion, leverages authoritative biomedical databases to enrich search queries with domain-specific entities. The second, Aspect-based Filter, identifies documents that are highly relevant to the query. The third, Diversity-based Score Reweighting, re-ranks these filtered documents by combining similarity and diversity scores, yielding more accurate results. Experimental tests on public biomedical IR datasets confirm that DPK-GLM significantly improves retrieval performance.

Author dc:creator

• Huang, Yizheng

Advisor dc:contributor.advisor

• Huang, Jimmy

• Information technology
• Artificial intelligence
• Bioinformatics

Statement dc:rights

• Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.

Language dc:language

en

Handle dc:identifier.uri

https://hdl.handle.net/10315/41736

OAI identifier oai:identifier

oai:yorkspace.library.yorku.ca:10315/41736