Applying local latent semantic indexing for information retrieval visualization
| dc.contributor.author | Miller, Michael Hugh | en_US |
| dc.date.accessioned | 2024-08-14T22:51:41Z | |
| dc.date.available | 2024-08-14T22:51:41Z | |
| dc.date.copyright | 1997 | en_US |
| dc.date.issued | 1997 | |
| dc.degree.department | School of Health Information Science | en_US |
| dc.degree.level | Master of Science M.Sc. | en |
| dc.description.abstract | Health professionals and consumers are not keeping pace with advancements in knowledge reported in the scientific literature. One of the reasons for this state of affairs is the lack of effective tools to search and present relevant information from MEDLINE and other literature sources. Current techniques are hindered by the ambiguities of natural language and the difficulty of presenting results from a search to the user effectively. This thesis presents a number of core methodologies for indexing and searching text databases such as MEDLINE and discusses the benefits and drawbacks of each method. Four techniques for visualizing search results are also presented and discussed including a novel method proposed by the author called Local Latent Semantic Indexing / Cluster (LLSI/Cluster). Experiments with LLSI/Cluster on three test collections of MEDLINE articles indicate that similar articles tend to cluster together. These findings suggest that LLSI/Cluster has potential as a visualization method for displaying a large set of documents to the user in a graphical manner. | |
| dc.format.extent | 110 pages | |
| dc.identifier.uri | https://hdl.handle.net/1828/19002 | |
| dc.rights | Available to the World Wide Web | en_US |
| dc.title | Applying local latent semantic indexing for information retrieval visualization | en_US |
| dc.type | Thesis | en_US |
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