Giovanni Colavizza, Frédéric Kaplan

On Mining Citations to Primary and Secondary Sources in Historiography

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Motivation - the Scholar

Sciences: Google Scholar

English Low-cost information gathering

Humanities: no Google Scholar like system

multiple languages High-cost information gathering

Issues: lack of data [Sula and Miller, 2014] leads to absence of services: estimated coverage of Web of Science for Humanities circa 13% [Mingers and Leydesdorff, 2015].

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Motivation - the Footnote

How humanists cite? Footnotes [see e.g. Hellqvist, 2009]

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Motivation - the Archive

Approximately half citations to primary sources [Wiberley Jr., 2009]

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Project: Linked Books

In the context of the Venice Time Machine

Partners: • Ca’ Foscari Library System • Biblioteca Marciana • Istituto Veneto di Scienze, Lettere ed Arti • Archivio di Stato di Venezia • EPFL

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Data acquisition

Corpus and annotation:

• 4 journals (for 150 years) • 2000 monographs • digitisation almost over • ongoing annotation (samples from 1

journal and 1’000 monographs done, approx 10’000 annotated citations)

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1- Data: new citation corpus (History of Venice) and a pipeline for citation extraction from footnotes.

2- Analytical framework: development or adaptation of methods from bibliometrics to the humanities.

3- Services: a Google Scholar for the History of Venice accounting for citations to both primary and secondary sources.

Goals

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Pipeline

Text block detection

Citation extraction

Citation parsing

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Text block detection

Method: SVM classifier. On what: text lines. Why? Citations are in footnotes, filter input space.

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Text block detection

Main causes of errors: - partial citations (e.g. “Ivi., p. 37. some text”)

FALSE NEGATIVE (critical) - in-text shortened citations FALSE POSITIVE

(not critical) - footnotes without citations FALSE NEGATIVE

(not critical)

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Next steps: finalise with extra features, layout detection on images.

Citation extraction

Method: CRF classifier. On what: words. Why? Citations need to be individuated and separated from text. Citations are classified in primary and secondary.

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Citation extraction

Main causes of error: - Wrong boundaries (instance accuracy

0.78, critical) - Wrong class (Primary-Secondary, not

critical)

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Next steps: finalise with extra features, add rules.

Citation parsing

Method: CRF classifier. On what: words. Why? Elements of citations need to be individuated to identify the cited source.

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Citation parsing

Main causes of error: - Wrong classes (e.g. Editor mistaken for

Author, not critical: reduce class space) - Sensible to under-represented classes

(e.g. archival terminology, not critical)

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Next steps: finalise with extra features, simplify class space, add rules, lookup features.

What’s next

Towards a pipeline for citation extraction:

1. Normalisation 2. Linkage 3. Database as linked data 4. Publication

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Giovanni Colavizza, Frédéric Kaplan

Thank you

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