Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 97-103).

Thesis (Ed. D.)--West Virginia University, 1989. / Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 101-106).

Thesis (Ph. D. in Library and Information Studies)--University of California, Berkeley, May 1991. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 226-244).

Thesis (doctoral)--Technische Universiteit Delft, 2003. / Includes bibliographical references (p. 167-181).

Thesis (Ph. D. in Library and Information Studies)--University of California, Berkeley, May 1991. / Includes bibliographical references (leaves 226-244).

Major applied research project (Ed. D.)--Nova Southeastern University, 1996. / Vita. Includes bibliographical references (leaves 65-71).

In this thesis, we propose a new approach to Chinese-English Biomedical cross-lingual information retrieval (CLIR) using query expansion based on the eCMeSH Tree, a Chinese-English ontology extended from the Chinese Medical Subject Headings (CMeSH) Tree. The CMeSH Tree is not designed for information retrieval (IR), since it only includes heading terms and has no term weighting scheme for these terms. Therefore, we design an algorithm, which employs a rule-based parsing technique combined with the C-value term extraction algorithm and a filtering technique based on mutual information, to extract Chinese synonyms for the corresponding heading terms. We also develop a term-weighting mechanism. Following the hierarchical structure of CMeSH, we extend the CMeSH Tree to the eCMeSH Tree with synonymous terms and their weights. We propose an algorithm to implement CLIR using the eCMeSH Tree terms to expand queries. In order to evaluate the retrieval improvements obtained from our approach, the results of the query expansion based on the eCMeSH Tree are individually compared with the results of the experiments of query expansion using the CMeSH Tree terms, query expansion using pseudo-relevance feedback, and document translation. We also evaluate the combinations of these three approaches. This study also investigates the factors which affect the CLIR performance, including a stemming algorithm, retrieval models, and word segmentation.

Thesis (M.S.) -- Mississippi State University. Department of Computer Science and Engineering. / Title from title screen. Includes bibliographical references.

Thesis (Ph. D.) -- University of Texas Southwestern Medical Center at Dallas, 2000. / Vita. Bibliography: 174-182.

Thesis (PhD)--Macquarie University, Australian Centre for Educational Studies, School of Education, 2009. / "2008". Bibliography: p. 268-283.