Scholarly Text Curation & Robust Anchoring Requirements Timothy - - PowerPoint PPT Presentation

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Scholarly Text Curation & Robust Anchoring Requirements Timothy - - PowerPoint PPT Presentation

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W3C Workshop on Annotations San Francisco, California USA 2 April 2014 Scholarly Text Curation & Robust Anchoring Requirements Timothy W. Cole (t-cole3@illinois.edu) Thomas G. Habing (thabing@illinois.edu) Anchoring Methods to Support

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Scholarly Text Curation & Robust Anchoring Requirements

Timothy W. Cole (t-cole3@illinois.edu) Thomas G. Habing (thabing@illinois.edu)

W3C Workshop on Annotations San Francisco, California USA 2 April 2014

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Anchoring Methods to Support Curatorial Annotation of Scholarly Text Resources

  • Should be fine-grained – for text this

means individual words and phrases

  • Should ensure persistence, e.g., even as

adjacent content is updated / corrected

  • Can be aligned across derivative formats and

serializations, even across repository boundaries

  • Can support search & replace, e.g., the target

is set of all instances found in a specific context

  • Should help distinguish curatorial annotations
  • f a specific digitization & its derivatives from

annotations of intellectual substance

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Use cases

  • Correction of OCR & manual transcriptions

– HathiTrust DL: 11 million digitized volumes automated OCR – Text Creation Partnership (TCP): 50,000 manually transcribed – Support corrections by curators, outside experts, the crowd

  • Correction of automated annotation, e.g., part-of-

speech tagging of TEI

  • Distinct from proposed targeting schemes for
  • ther kinds of scholarly use cases, e.g., commentary

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Why Annotations?

  • So proposed corrections can be reviewed

and themselves annotated as needed

  • To share with other repositories
  • To maintain portability of provenance

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Veridian Digital Library Software

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Align across representations

  • Treat the OCR as an annotation of a

segment of PDF / JPEG page image

– Annotating agent is OCR program

  • Proposed OCR correction is then an

annotation of the OCR annotation of the page image

  • Complicating factor – OCR outputs at page

level, correction is usually done at line level

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Annotating repeated errors?

The string “Jrbana” appears 782 times in OCR texts of the Urbana Daily Courier (1903-1935) Do we need to require that users find every instance of “Jrbana”....? Can we have search-and-replace annotations?

* “Urbana” appears ~ 200,000 times

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http://annolex.at.northwestern.edu/

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From TCP-EEBO Collection as hosted at the University of Michigan

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<div class="sp"> <div class="speaker">Valer.</div> <p>Oh Collatin<span class="gap">•…</span>! I am a true Cittizen and in this I will best shew my selfe to be one, to take part with the stronger. If <span class="rend-italic">Se<span class="gap">•…</span>ius</span>

  • re-come, I am Liegeman to <span class="rend-italic">Serutus,</span>

&amp; if <span class="rend-italic">Ta<span class="gap">•…</span>quin</span> subdue, I am for <span class="rend-italic">Viue Tarquinius.</span> </p> </div>

//*[@id="doccontent"]/div/div[39]

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Relation between anchoring method & what’s being targeted

  • 1st challenge – understanding the annotator’s

intention.

  • 2nd challenge – using a targeting approach that is

consistent with annotator’s intent

  • Some schemes limit possible range of interpretations

– Chapter, verse & line approaches (e.g., CTS)

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Anchoring Methods to Support Curatorial Annotation of Scholarly Text Resources

  • Should be fine-grained – for text this

means individual words and phrases

  • Should ensure persistence, e.g., even as

adjacent content is updated / corrected

  • Can be aligned across derivative formats and

serializations, even across repository boundaries

  • Can support search & replace, e.g., the target

is set of all instances found in a specific context

  • Should help distinguish curatorial annotations
  • f a specific digitization & its derivatives from

annotations of intellectual substance

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