Persistent Link:
http://hdl.handle.net/10150/106476
Title:
Visualizing Similarity in Subject Term Co-Assignment
Author:
Gabel, Jeff; Smiraglia, Richard P.
Editors:
Breitenstein, Mikel; Loschko, Cheryl Lin
Citation:
Visualizing Similarity in Subject Term Co-Assignment 2009,
Issue Date:
2009
URI:
http://hdl.handle.net/10150/106476
Submitted date:
2009-11-06
Abstract:
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.
Type:
Conference Paper
Language:
en
Keywords:
Indexing; Citation Analysis; Knowledge Organization; Linguistics
Local subject classification:
ASIST; SIG-CR

Full metadata record

DC FieldValue Language
dc.contributor.authorGabel, Jeffen_US
dc.contributor.authorSmiraglia, Richard P.en_US
dc.contributor.editorBreitenstein, Mikelen_US
dc.contributor.editorLoschko, Cheryl Linen_US
dc.date.accessioned2009-11-06T00:00:01Z-
dc.date.available2010-06-18T23:48:09Z-
dc.date.issued2009en_US
dc.date.submitted2009-11-06en_US
dc.identifier.citationVisualizing Similarity in Subject Term Co-Assignment 2009,en_US
dc.identifier.urihttp://hdl.handle.net/10150/106476-
dc.description.abstractThe 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.en_US
dc.format.mimetypedocen_US
dc.language.isoenen_US
dc.subjectIndexingen_US
dc.subjectCitation Analysisen_US
dc.subjectKnowledge Organizationen_US
dc.subjectLinguisticsen_US
dc.subject.otherASISTen_US
dc.subject.otherSIG-CRen_US
dc.titleVisualizing Similarity in Subject Term Co-Assignmenten_US
dc.typeConference Paperen_US
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