1 / 16

Enrichment and Structuring of Archival Description Metadata

Enrichment and Structuring of Archival Description Metadata. Kalliopi Zervanou*, Ioannis Korkontzelos**, Antal van den Bosch* & Sophia Ananiadou**. ** National Centre for Text Mining The University of Manchester, UK Ioannis.Korkontzelos@manchester.ac.uk Sophia.Ananiadou@manchester.ac.uk.

lucky
Télécharger la présentation

Enrichment and Structuring of Archival Description Metadata

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Enrichment and Structuring of Archival Description Metadata Kalliopi Zervanou*, Ioannis Korkontzelos**, Antal van den Bosch* & Sophia Ananiadou** ** National Centre for Text Mining The University of Manchester, UK Ioannis.Korkontzelos@manchester.ac.uk Sophia.Ananiadou@manchester.ac.uk * Tilburg Centre for Cognition & Communication The University of Tilburg, NL K.Zervanou@uvt.nl Antal.vdnBosch@uvt.nl

  2. Research on Metadata • Developing standards: • collection specific (e.g. EAD, MARC21) • cross-collection (e.g. Dublin Core) • Provide mappings: • across schemas • ontologies (ad hoc or standard CDOC-CRM) • Discard metadata for IR (Koolen et al., 2007) • Exploit metadata for IR (Zhang&Kamps, 2009)

  3. The IISH EAD dataset • EAD: XML standard for encoding archival descriptions • Challenges: • Variety of languages used • Varying type and amount of information • Style: enumerations, lists, incomplete sentences

  4. Motivation & Objectives • Improved search and retrieval • content-based metadata document clustering • content-based/semantic search • support exploratory search • link across collections, metadata formats & institutions • create unified metadata knowledge resources

  5. Method overview

  6. Method overview

  7. Pre-processing • EAD/XML element selection & extraction • EAD elements containing free-text & archive content information • Language identification (n-gram method) • Identifier trained on Europarl corpus • Text snippets length: ~20 tokens

  8. Snippet length based on language

  9. Method overview

  10. Method overview

  11. Enrichment & Structuring • Topic detection: Automatic term recognition using C-value method • Agglomerative hierarchical term clustering: • complete, single & average linkage criteria • document co-occurence & lexical similarity measures

  12. Method overview

  13. Method overview

  14. Term results (auto eval)

  15. Results • C-value best performance: candidates that occur as non-nested at least once • Average linkage criterion & Doc Co-occurence: provide broader and richer hierarchies

  16. Questions? Check-out our poster!

More Related