Award Date

5-15-2026

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Committee Member

Shaikh Arifuzzaman

Second Committee Member

Mingon Kang

Third Committee Member

Laxmi Gewali

Fourth Committee Member

Fatma Nasoz

Fifth Committee Member

Xue Xing

Number of Pages

50

Abstract

Temporal information extraction plays a critical role in the biomedical domain, where the ability to identify events and their temporal relationships is central to interpreting research findings. However, annotated corpora for this task remain scarce and costly to produce and the existing models developed for clinical text do not transfer well. This work bridges that gap through iterative silver-label refinement. A temporal model originally trained on news-domain data is adapted to biomedical text through cycles of automatic labeling, targeted correction, and retraining without the need for comprehensive manual annotation.

Key contributions include a practical iterative refinement methodology demonstrating that the correction-to-silver-label ratio is critical to domain adaptation success. Additionally, it introduces multi-class temporal relation classification across six relation types and a conversion workflow for adapting existing temporal corpora to modern transformer architectures. Both named entity recognition (NER) and relation extraction (RE) performance improved meaningfully across iterations, with the approach successfully overcoming substantial label distribution differences between the source and target domains. The data-centric methodology may be applicable to other domains facing similar resource constraints.

Keywords

domain adaptation; named entity recognition; relation extraction; self-training; silver labeling

Disciplines

Computer Sciences | Other Computer Sciences | Physical Sciences and Mathematics

File Format

PDF

File Size

538 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

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