The purpose of this research is to improve retrieval performance in systems that use assigned subject descriptors, such as library subject headings. We are looking for wider semantic boundaries surrounding summary headings assigned to documents by providing a means of identifying clustered headings that fall within the indexerâ s collective common perceptions of relevance. We are here experimenting with two techniques that can help increase both precision and recall. In earlier research citationâ chasing was employed to yield a fuller retrieval set than might have been found using subject headings alone. In the present study we are employing multiâ dimensional scaling to determine the best fit among works to which subject descriptors have been coâ assigned. A term co-occurrence matrix compiled from 19 LCSH subject headings assigned to works in the field of â language originâ is used to generate an MDS map of the semantic space. Two clusters emerge: language and languages, and evolution biology, sometimes termed evolingo. Results allow us to visualize how differing perceptions of indexers affect the semantic space surrounding assigned terms. In both cases - citation-chasing and term co-occurrence - and especially when combining the two techniques acting as thresholds for each other, it is possible to overcome the inverse relation between precision and recall.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/106476 |
| Date | January 2009 |
| Creators | Gabel, Jeff, Smiraglia, Richard P. |
| Contributors | Breitenstein, Mikel, Loschko, Cheryl Lin |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Conference Paper |
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