enrichment and structuring of archival description metadata

ACL/LaTeCH-Portland, June 24th 2011

Enrichment and Structuring of Archival Description

Metadata

Kalliopi Zervanou*, Ioannis Korkontzelos**,

Antal van den Bosch* & Sophia Ananiadou** * Tilburg Centre for Cognition &

CommunicationThe University of Tilburg, NL

K.Zervanou@uvt.nl Antal.vdnBosch@uvt.nl

** National Centre for Text MiningThe University of Manchester, UK

Ioannis.Korkontzelos@manchester.ac.uk Sophia.Ananiadou@manchester.ac.uk

ACL/LaTeCH-Portland, June 24th 2011

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)

ACL/LaTeCH-Portland, June 24th 2011

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

ACL/LaTeCH-Portland, June 24th 2011

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

ACL/LaTeCH-Portland, June 24th 2011

Method overview

ACL/LaTeCH-Portland, June 24th 2011

Method overview

ACL/LaTeCH-Portland, June 24th 2011

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

ACL/LaTeCH-Portland, June 24th 2011

Snippet length based on language

ACL/LaTeCH-Portland, June 24th 2011

Method overview

ACL/LaTeCH-Portland, June 24th 2011

Method overview

ACL/LaTeCH-Portland, June 24th 2011

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

ACL/LaTeCH-Portland, June 24th 2011

Method overview

ACL/LaTeCH-Portland, June 24th 2011

Method overview

ACL/LaTeCH-Portland, June 24th 2011

Term results (auto eval)

ACL/LaTeCH-Portland, June 24th 2011

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

Questions?Check-out our poster!