{"id":{"repo_id":"york","oai_identifier":"oai:yorkspace.library.yorku.ca:10315/41736"},"canonical_url":"https://search.dev.ndltd.org/etd/york/oai:yorkspace.library.yorku.ca:10315/41736","repository":{"repo_id":"york","name":"York University","base_url":"https://yorkspace.library.yorku.ca/oai/request"},"display":{"title":"Enhancing General Language Models for Biomedical Test Retrieval via Diversified Prior Knowledge","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Huang, Yizheng"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Huang, Jimmy"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12-08","date_published":"2023-12-08","updated_at":"2026-07-24T06:34:05Z","subjects":["Information technology","Artificial intelligence","Bioinformatics"],"languages":["en"],"rights":["Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10315/41736","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Huang, Jimmy"]},{"key":"dc:creator","label":"Author","values":["Huang, Yizheng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-12-08T14:42:10Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-12-08T14:42:10Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-12-08"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Information technology","Artificial intelligence","Bioinformatics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10315/41736"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Enhancing General Language Models for Biomedical Test Retrieval via Diversified Prior Knowledge"]}]}],"canonical_facts":{"dc:contributor.advisor":["Huang, Jimmy"],"dc:creator":["Huang, Yizheng"],"dc:date.accessioned":["2023-12-08T14:42:10Z"],"dc:date.available":["2023-12-08T14:42:10Z"],"dc:date.issued":["2023-12-08"],"dc:description.abstract":["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."],"dc:identifier.uri":["https://hdl.handle.net/10315/41736"],"dc:language":["en"],"dc:rights":["Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests."],"dc:subject":["Information technology","Artificial intelligence","Bioinformatics"],"dc:title":["Enhancing General Language Models for Biomedical Test Retrieval via Diversified Prior Knowledge"],"dc:type":["Electronic Thesis or Dissertation"]},"updated_at":"2026-07-24T06:34:05Z"}