{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/79362"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/79362","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Algorithms for Relation Extraction from Biomedical Texts","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Liu, Sijia; 0000-0001-9763-1164"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Chaudhary, Vipin","Computer Science and Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-04-04T20:30:51Z","date_published":"2019-04-04T20:30:51Z","updated_at":"2026-07-27T19:05:16Z","subjects":["computer science"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/79362","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chaudhary, Vipin","Computer Science and Engineering"]},{"key":"dc:creator","label":"Author","values":["Liu, Sijia; 0000-0001-9763-1164"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-04-04T20:30:51Z","2019","2018-12-26 11:18:30"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computer science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/79362"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","The boost in the capacity and volume of biomedical texts, including biomedical literatures and electronic health records (EHRs), has created a tremendous opportunity for biomedical research and practice. It is widely acknowledged that relation extraction of unstructured textual contents using natural language processing (NLP) and text mining techniques is essential for using biomedical data for secondary purposes, which leads to the increasing demands on NLP systems and approaches. Relation extraction is defined as the task to resolve the relations among the mentioned entities in the textual context. The diversity and complexity of semantic relations from biomedical texts make it challenging for a unified solution to different tasks. This motivates us to strive towards the development of diverse approaches, including distant supervised, rule-based and deep learning methods, to resolve biomedical relation extraction problems. Towards this end, the dissertation consists of proposed solutions to several relation extraction tasks in biomedical domain: 1) Coreference Resolution: we proposed an infinite mixture model to resolve coreferent relations among mentions in clinical notes. A similarity measure function is proposed to determine the coreferent relations. 2) Drug-Drug Interaction: we proposed a relation classification framework based on topic modeling augmented with distant supervision for the task of DDI from biomedical text. Our approach does not require human efforts such as annotation and labeling, which is its advantage in trending big data applications comparing with other approaches. 3) Event Time Association of Lab Test Results: we proposed a rule-based relation extraction system to extract the relations between lab test results and temporal information from clinical texts. 4) Chemical Protein Relation: we proposed an attention-based neural networks method to extract interaction information between chemical, genes and proteins (ChemProt). The attention weight distribution and top attention words show that the attention mechanism is effective in highlighting semantic association and textual variants of ChemProt relations without prior domain knowledge and extensive feature engineering."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Algorithms for Relation Extraction from Biomedical Texts"]}]}],"canonical_facts":{"dc:contributor":["Chaudhary, Vipin","Computer Science and Engineering"],"dc:creator":["Liu, Sijia; 0000-0001-9763-1164"],"dc:date":["2019-04-04T20:30:51Z","2019","2018-12-26 11:18:30"],"dc:description":["Ph.D.","The boost in the capacity and volume of biomedical texts, including biomedical literatures and electronic health records (EHRs), has created a tremendous opportunity for biomedical research and practice. It is widely acknowledged that relation extraction of unstructured textual contents using natural language processing (NLP) and text mining techniques is essential for using biomedical data for secondary purposes, which leads to the increasing demands on NLP systems and approaches. Relation extraction is defined as the task to resolve the relations among the mentioned entities in the textual context. The diversity and complexity of semantic relations from biomedical texts make it challenging for a unified solution to different tasks. This motivates us to strive towards the development of diverse approaches, including distant supervised, rule-based and deep learning methods, to resolve biomedical relation extraction problems. Towards this end, the dissertation consists of proposed solutions to several relation extraction tasks in biomedical domain: 1) Coreference Resolution: we proposed an infinite mixture model to resolve coreferent relations among mentions in clinical notes. A similarity measure function is proposed to determine the coreferent relations. 2) Drug-Drug Interaction: we proposed a relation classification framework based on topic modeling augmented with distant supervision for the task of DDI from biomedical text. Our approach does not require human efforts such as annotation and labeling, which is its advantage in trending big data applications comparing with other approaches. 3) Event Time Association of Lab Test Results: we proposed a rule-based relation extraction system to extract the relations between lab test results and temporal information from clinical texts. 4) Chemical Protein Relation: we proposed an attention-based neural networks method to extract interaction information between chemical, genes and proteins (ChemProt). The attention weight distribution and top attention words show that the attention mechanism is effective in highlighting semantic association and textual variants of ChemProt relations without prior domain knowledge and extensive feature engineering."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/79362"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["computer science"],"dc:title":["Algorithms for Relation Extraction from Biomedical Texts"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:16Z"}