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#kbdata: Exploring potential impact of technology limitations on DH research

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Talk I gave at the Dutch National Library on Nov 3, 2015

(Slides made by Myriam Traub, any mistakes are my bad)

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#kbdata: Exploring potential impact of technology limitations on DH research

  1. 1. #kbdata: Exploring potential impact of technology limitations on DH research Myriam C. Traub, Jacco van Ossenbruggen Centrum Wiskunde & Informatica, Amsterdam
  2. 2. Translate the established tradition of source criticism to the digital world and create a new tradition of tool criticism to systematically identify and explain technology-induced bias.
 http://event.cwi.nl/toolcriticism/ #toolcrit 2
  3. 3. Context ✤ SealincMedia project, original goals: ✤ crowdsourcing enrichment ✤ measure effect on scholarly tasks ✤ Who are the scholars? ✤ What are their tasks? 3
  4. 4. Interviews ✤ Aim: ✤ Find out what types of research tasks scholars perform on digital archives ✤ Which quantitative / distant reading tasks are not (sufficiently) supported ✤ Scholars with experience in performing historical research on digital archives 4 (seeTPDL 2015 paper for details)
  5. 5. 5 I mostly use digital archives for exploration of a topic, selecting material for close reading (T1, T2) or external processing (T4). OCR quality in digital archives / libraries is partly very bad. I cannot quantify its impact on my research tasks. I would not trust quantitative analyses (T3a, T3b) based on this data sufficiently to use it in publications.
  6. 6. Categorisation of research tasks T1 find the first mention of a concept T2 find a subset with relevant documents T3 investigate quantitative results over time T3.a compare quantitative results for two terms T3.b compare quantitative results from two corpora T4 tasks using external tools on archive data
  7. 7. Literature ✤ OCR quality is addressed from the perspective of the collection owner/OCR software developer ✤ Usability studies for digital libraries ✤ Robustness of search engines towards OCR errors ✤ Error removal in post- processing either systematically or intellectually 7
  8. 8. We care about average performance on representative subsets for generic cases. I care about actual performance on my non- representative subset for my specific query. 8 Two different perspectives of quality evaluation
  9. 9. Use case ✤ Aims: ✤ To study the impact on research tasks in detail ✤ Identify starting points for workarounds and/or further research ✤ Tasks T1 - T3 9
  10. 10. T1: Finding the first mention ✤ Key requirement: recall ✤ 100% recall is unrealistic ✤ Aim: Find out how a scholar can assess the reliability of results 10
  11. 11. “Amsterdam” 1642 11 First mention of … … in the OCRed newspaper archive of the KB? 1618 earliest document
  12. 12. O C R pre-processing post-processing ingestion scanning 12 Understanding potential sources of bias and errors ✤ many details difficult to reconstruct ✤ essential to understand overall impact
  13. 13. “Amsterdam” 1642 13 First mention of … … in the OCRed newspaper archive of the KB? 1618 earliest document “Amfterdam” 1624
  14. 14. 01 OCR confidence values useful? ✤ Available for all items in the collection: page, word, character ✤ Only for highest ranked words / characters, other candidates missing ✤ This information would be required to estimate recall. 14
  15. 15. Confusion table ✤ Applied frequent OCR confusions to query ✤ 23 alternative spellings, but none of them yielded an earlier mention ✤ Problem: long tail Amstcrdam 16-01-1743 Amstordam 01-08-1772 Amsttrdam 04-08-1705 Amslerdam 12-12-1673 Amslcrdam 20-06-1797 Amslordam 29-06-1813 Amsltrdam 13-04-1810 Amscerdam 17-10-1753 Amsccrdam 16-02-1816 Amscordam 01-11-1813 Amsctrdam 16-06-1823 Amfterdam already found Amftcrdam 17-08-1644 Amftordam 31-01-1749 Amfttrdam 26-11-1675 Amflerdam 03-03-1629 Amflcrdam 01-03-1663 Amflordam 05-03-1723 Amfltrdam 01-09-1672 Amfcerdam 22-04-1700 Amfccrdam 27-11-1742 Amfcordam - Amfctrdam 09-10-1880 correct confused s f n u e c n a t l t c h b l i e o e t full table available online: http://dx.doi.org/10.6084/m9.figshare.1448810
  16. 16. “Amsterdam” 1642 “Amfterdam” 1624 “Amsterstam” 1618 16 First mention of … 1618 … in the OCRed newspaper archive of the KB? earliest document
  17. 17. “Amsterdam” 1642 “Amfterdam” 1624 “Amsterstam” 1618 17 Update! 1618 Corrections for 17th century newspapers were crowdsourced! earliest document “Amsterdam” 1620
  18. 18. … but why not 1618?
  19. 19. Confusion Matrix OCR Confidence Values Alternative Confidence Values available: sample only full corpus not available T1 find all queries for x, impractical estimated precision, not helpful improve recall T2 as above estimated precision, requires improved UI improve recall T3 pattern summarized over set of alternative queries estimates of corrected precision estimates of corrected recall T3.a warn for different susceptibility to errors as above, warn for different distribution of confidence values as above T3.b as above as above as above 19
  20. 20. Conclusions Problems ✤ Scholars see OCR quality as a serious problem, but cannot assess its impact ✤ OCR technology is unlikely to be perfect ✤ OCR errors are reported in terms of averages measured over representative samples ✤ Impact on a specific research task cannot be assessed based on average error metrics Start of solutions ✤ Impact of OCR is different for different research tasks, so these tasks need to made be explicit ✤ OCR errors often assumed to be random but are often partly systematic ✤ Tool pipelines and their limitations need to be transparent & better documented
  21. 21. No silver bullet ✤ we propose novel strategies that solve part of the problem: ✤ critical attitude (awareness and better support) ✤ transparency (provenance, open source, documentation, …) ✤ alternative quality metrics (taking research context into account) 21
  22. 22. ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 5000000 10000000 15000000 1700 1800 1900 2000 decades numberofdocuments Viewed documents (blue) compared to overall corpus size (red) RQ: Is this tiny fragment biased by technology?
  23. 23. User logs ✤ 5 months on 8 servers ✤ March - July 2015 ✤ 100 M requests ✤ 4 M queries ✤ 1 M unique queries 
 (dominated by named entities) ✤ 2.7 M unique documents viewed
  24. 24. http://resolver.kb.nl/resolve?urn=ddd:011010313 March - July 2015. 24 Top viewed documents 1. views: 700 2. views: 243 3. views: 189 http://resolver.kb.nl/resolve?urn=ddd:010775269http://resolver.kb.nl/resolve?urn=ddd:011148923
  25. 25. Top 25 queries (# IP hashes) 493 armeense 283 telegraaf 200 doodvonnis batavia 176 ajax 168 voetbal 166 nieuwsblad van het noorden 149 suriname 142 oorlog 132 hitler 132 vvd PROX complot 131 amsterdam 129 volkskrant 126 algemeen handelsblad 122 armeensche 119 limburgs dagblad 119 de telegraaf 114 zoetemelk 114 rotterdam 114 20e eeuw 113 het vrije volk 112 staatscourant 112 brand 108 de waarheid 103 soekaboemi 97 overleden
  26. 26. Can we measure bias 
 in all queries?
  27. 27. Candidate metric to measure search bias ✤ Retrievability 
 (IR, Azzopardi, CIKM 2008) ✤ measures how often documents are retrieved for a given set Q ✤ compares popular documents against non-popular ✤ Inequality expressed with Gini coefficient and Lorenz curve ✤ Inequality correlated with user interest is fine…
  28. 28. Experimental setup ✤ Repeat original experiment with synthesised queries ✤ Run experiment with real queries from log ✤ note the ratio: 1M queries vs 100M documents ✤ To do: test known item search for different quality OCR, different media, different titles, …
  29. 29. Lorenz curves c=10, Gini=0.97 c=100, Gini=0.90 c=1000, Gini=0.78
  30. 30. 0 1 5 10 50 100 500 1000 5000 10000 50000 1 2 3 4 5 6 7 8 9 10 ret_score counts_16_log 0 1 5 10 50 100 500 1000 5000 10000 50000 100000 500000 1000000 1 2 3 4 5 6 7 8 9 10 ret_score counts_17_log 0 1 5 10 50 100 500 1000 5000 10000 50000 100000 500000 1000000 5000000 1 2 3 4 5 6 7 8 9 10 ret_score counts_18_log 0 1 5 10 50 100 500 1000 5000 10000 50000 100000 500000 1000000 5000000 1 2 3 4 5 6 7 8 9 10 ret_score counts_18_log
  31. 31. For documents that were viewed at least once. OCR page confidence values (x) and number of views by users (y) 33
  32. 32. 0.00 0.25 0.50 0.75 1.00 1700 1800 1900 2000 decades percentagesofr(d) r(d) 0 1 2 3 4 0.0 0.2 0.4 0.6 0.8 1700 1800 1900 2000 decades percentagesofr(d) r(d) 0 1 2 3 4 0.1 0.2 0.3 0.4 1700 1800 1900 2000 decades percentagesofr(d) r c=10 c=100 c=1000
  33. 33. 0.00 0.05 0.10 0.15 1700 1800 1900 2000 decades percentagesofr(d) r(d) 0 1 2 3 4
  34. 34. ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 5000000 10000000 15000000 1700 1800 1900 2000 decades numberofdocuments Viewed documents compared to overall corpus size (per decade) RQ: Is this tiny fragment biased by technology?
  35. 35. ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1024 16384 262144 4194304 1700 1800 1900 2000 decades numberofdocuments # do # do
  36. 36. Conclusions ✤ Only small fragment of newspaper corpus is viewed or even retrieved in top #10, 100, 100 ✤ No clear evidence retrieval bias is correlated with OCR errors. Why? ✤ there is no relation ✤ we look for patterns at a too generic level ✤ back to the specificity of the use cases? ✤ Other forms of bias that are measurable/quantifiable?

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