HuC lecture - Digital and Humanities: Continuing the Conversation
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Lecture given at the KNAW Humanities Cluster introducing the Digital Humanities Group and ideas for collaborations
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HuC lecture - Digital and Humanities: Continuing the Conversation
- 1. Digital and Humanities: Continuing the Conversation Marieke van Erp merpeltje
- 2. Aims of this talk • Introduce the new Digital Humanities Group • Discussing some ideas for digital humanities at HuC • Starting the conversation
- 3. Digital Humanities Group • Our research is to develop new language technology methods for the humanities • Focus on big ‘textual’ data • Interdisciplinary • Inter-institutional (joint research group of Huygens ING, IISH and Meertens Institute) Melvin Wevers Adina Nerghes Marieke van Erp
- 4. Some ideas for Digital Humanities at HuC
- 5. What is Digital Humanities “Digital humanities is work at the intersection of digital technology and humanities disciplines.” Johanna Drucker, 2013 “Bringing computational methods to bear on traditional humanities scholarship.” Elijah Meeks, 2016 “Digital humanities is a diverse and still emerging field that encompasses the practice of humanities research in and through information technology, and the exploration of how the humanities may evolve through their engagement with technology, media, and computational methods.” Digital Humanities Quarterly, 2017
- 6. WebSci’12 Folgert Karsdorp, Antal van den Bosch “The Structure and Evolution of Story Networks.” Royal Society Open Science 3 (2016): 160071. Stapel, R. (2016). Reconstruction of Labour Relations in the North Sea Region in the Late Middle Ages: Spatio-Temporal Analysis Using Historical GIS, Taxation Sources, and Coin Finds. In Digital Humanities 2016: Conference Abstracts. Jagiellonian University & Pedagogical University, Kraków, pp. 366-369.
- 7. What is Digital Humanities? Humanities Technology Data
- 8. What is Digital Humanities? Humanities Technology Data
- 10. Federalist papers • In 1788 Alexander Hamilton, James Madison en John Jay wrote 85 arguments supporting the American constitution using the penname ‘Publius’ • Until1962 it was unknown who had written what • One of the first examples of authorship attribution using statistics https://priceonomics.com/how-statistics-solved-a-175-year-old-mystery-about/
- 11. Federalist papers • Sentence length by Hamilton & Madison was ~35 words • Hamilton preferred to use ‘while’ • Madison preferred to use ‘whilst’ • But sometimes Hamilton also used ‘whilst’ and v.v.
- 12. Federalist papers • Frequency analysis in 1959: Then the words were entered into the IBM7090 that could analyse 3000 words per batch
- 13. Federalist papers • In the ‘known’ Hamilton documents ‘upon’ occurred 3.24 times per 1000 words (on average). Madison used that word far less often. Thus, when the word ‘upon’ occurs more often in an ‘unknown’ document, the chance is higher that it was written by Hamilton. • Such comparisons were also made for other words • According to this analysis, the majority of the documents was written by James Madison
- 14. Contemporary frequency analysis Thanks paai!
- 15. Dimensions of DH • Data cleaning • Knowledge modelling • Analysis • Information Extraction • Enrichment • Visualisation • Reflection • Evaluation • … Image source: http://www.mcescher.com/wp-content/uploads/2013/10/LW359-MC-Escher-Stars-1948.jpg
- 18. Knowledge modelling DH@HuC • Possible research directions: • Historical narratives • Literary narratives • Challenges: • What is a (historical/literary) narrative? (active research field in CS, e.g. CMN workshops) • How can we automatically detect narratives? • Can we detect and model storylines? (with Tommaso Caselli, RUG?)
- 19. Trend Analysis • Trace use of `toxic’ metaphors around the financial crisis in newspapers • Detect metaphors and their context through a semantic network • Nerghes, A., Hellsten, I., and Groenewegen, P. (2015) A Toxic Crisis: Metaphorizing the Financial Crisis. International Journal Of Communication, 9(27).
- 20. Trend Analysis DH@HuC • Possible research directions: • Metaphors in Dutch folk songs • Concepts in Resolutions of Dutch States General • Challenges: • Sparser and more varied data • Language variation through time
- 21. Tracing and mapping ‘integration’
- 22. Visualising • Can help researchers explore data • Discover patterns • Discover outliers • Can help communicate our research
- 23. Tracing and mapping DH@HuC • Possible research directions: • Tracing concept drift in Dutch strikes • Mapping concepts in folk tales • Challenges: • Data sparsity • Figurative language use
- 27. Semantic Querying edit the query
- 28. Semantic Querying/Cross-linguality DH@HuC • Possible research directions: • Linking sources on global trade • Mapping concepts in letters • Challenges: • Archaic language use • Domain specific concepts
- 29. Evaluation: Social networks from novels • Named entity recognition is often used to generate social networks from novels • But: most work is done on ‘the classics’, how well does it perform on contemporary novels? MSc thesis Niels Dekker
- 30. Evaluation: Social networks from novels ChalaisChalais M. BonacieuxM. Bonacieux de M. Busignyde M. Busigny Houdiniere LaHoudiniere La John FeltonJohn Felton Bois-Tracy de Ma...Bois-Tracy de Ma... de M. Schombergde M. Schomberg LubinLubin Porthos MonsieurPorthos Monsieur la Harpe de Ruela Harpe de Rue RochellaisRochellais Richelieu deRichelieu de de Busigny Monsi...de Busigny Monsi... Milady ClarikMilady Clarik RochefortRochefort d Monsieurd Monsieur M. CoquenardM. Coquenard de Treville Mons...de Treville Mons... Mr. FeltonMr. Felton MontagueMontague dâArtagnan Mon...dâArtagnan Mon... Buckingham de Mo...Buckingham de Mo... de Monsieur Voit...de Monsieur Voit... Monsieur Bernajo...Monsieur Bernajo... III HenryIII Henry Monsieur Dessess...Monsieur Dessess... de Chevreuse Mad...de Chevreuse Mad... Donna EstafaniaDonna Estafania Lord DukeLord Duke Quixote DonQuixote Don Lorme de MarionLorme de Marion de Cahusac Monsi...de Cahusac Monsi... BazinBazin Chevalier Monsie...Chevalier Monsie... MusketeerMusketeer Constance Bonaci...Constance Bonaci... M. DessessartM. Dessessart GermainGermain de M. Cavoisde M. Cavois JudithJudith GasconGascon MousquetonMousqueton Monsieur AthosMonsieur Athos Duke MonsieurDuke Monsieur Charlotte BacksonCharlotte Backson BethuneBethune Planchet MonsieurPlanchet Monsieur Louis XIIILouis XIII Bonacieux MadameBonacieux Madame de Benserade Mon...de Benserade Mon... GervaisGervais MeungMeung Chesnaye LaChesnaye La Bonacieux Monsie..Bonacieux Monsie.. ChrysostomChrysostom Wardes de De M.Wardes de De M. Coquenard Monsie...Coquenard Monsie... PatrickPatrick BerryBerry MandeMande Laporte M.Laporte M. de M. Laffemasde M. Laffemas Laporte MonsieurLaporte Monsieur Louis XIVLouis XIV AnneAnne de M. Tremouille...de M. Tremouille... NormanNorman de M. Bassompier...de M. Bassompier... IV HenryIV Henry Villiers GeorgeVilliers George BearnaisBearnais I CharlesI Charles PierrePierre monsieur Aramis ...monsieur Aramis ... JussacJussac DenisDenis GasconsGascons Coquenard MadameCoquenard Madame CrevecoeurCrevecoeur PicardPicard pope Popepope Pope de M. Trevillede M. Treville de Marie Mde Marie M LorraineLorraine #N/A#N/A Cardinal MonsieurCardinal Monsieur FourreauFourreau BicaratBicarat Marie Michon MAR...Marie Michon MAR... Lord de WinterLord de Winter Milady de De Win...Milady de De Win... M. dâArtagnanM. dâArtagnan DukeDuke Messieurs PorthosMessieurs Porthos KittyKitty MSc thesis Niels Dekker
- 31. Evaluation: Social networks from novels ChalaisChalais M. BonacieuxM. Bonacieux de M. Busignyde M. Busigny Houdiniere LaHoudiniere La John FeltonJohn Felton Bois-Tracy de Ma...Bois-Tracy de Ma... de M. Schombergde M. Schomberg LubinLubin Porthos MonsieurPorthos Monsieur la Harpe de Ruela Harpe de Rue RochellaisRochellais de Marie Medicisde Marie Medicis de Busigny Monsi...de Busigny Monsi... Milady ClarikMilady Clarik RochefortRochefort Grimaud MonsieurGrimaud Monsieur M. CoquenardM. Coquenard de Treville Mons...de Treville Mons... Commissary Monsi...Commissary Monsi... Mr. FeltonMr. Felton MontagueMontague Buckingham de Mo...Buckingham de Mo... de Monsieur Voit...de Monsieur Voit... M. DartagnanM. Dartagnan Monsieur Bernajo...Monsieur Bernajo... III HenryIII Henry Monsieur Dessess...Monsieur Dessess... de Chevreuse Mad...de Chevreuse Mad... Donna EstafaniaDonna Estafania Lord DukeLord Duke Quixote DonQuixote Don Lorme de MarionLorme de Marion de Cahusac Monsi...de Cahusac Monsi... BazinBazin Chevalier Monsie...Chevalier Monsie... MusketeerMusketeer M. DessessartM. Dessessart GermainGermain de M. Cavoisde M. Cavois JudithJudith Monsieur Dartagn...Monsieur Dartagn... GasconGascon MousquetonMousqueton Monsieur AthosMonsieur Athos Duke MonsieurDuke Monsieur Charlotte BacksonCharlotte Backson BethuneBethune Planchet MonsieurPlanchet Monsieur Louis XIIILouis XIII Milady de WinterMilady de Winter Bonacieux MadameBonacieux Madame de Benserade Mon...de Benserade Mon... GervaisGervais MeungMeung Chesnaye LaChesnaye La Bonacieux Monsie...Bonacieux Monsie... ChrysostomChrysostom Wardes de De M.Wardes de De M. Coquenard Monsie...Coquenard Monsie... PatrickPatrick Lord de De WinterLord de De Winter BerryBerry MandeMande Laporte M.Laporte M. Richelieu deRichelieu de GodeauGodeau Laporte MonsieurLaporte Monsieur Louis XIVLouis XIV AnneAnne de M. Tremouille...de M. Tremouille... NormanNorman de M. Bassompier...de M. Bassompier... IV HenryIV Henry Villiers GeorgeVilliers George de M. Laffemasde M. Laffemas BearnaisBearnais PierrePierre monsieur Aramis ...monsieur Aramis ... JussacJussac DenisDenis GasconsGascons CrevecoeurCrevecoeur PicardPicard pope Popepope Pope de M. Trevillede M. Treville de Monsieur Cavo...de Monsieur Cavo... LorraineLorraine Dangouleme DucDangouleme Duc #N/A#N/A Cardinal MonsieurCardinal Monsieur FourreauFourreau BicaratBicarat Marie Michon MAR...Marie Michon MAR... I CharlesI CharlesDukeDuke VilleroyVilleroy Messieurs PorthosMessieurs Porthos KittyKitty Bonacieux Consta...Bonacieux Consta... After changing d’Artagnan to Dartagnan: (and the F-score rose from 0.13 to 0.53) MSc thesis Niels Dekker
- 32. Replicability and Reproducibility • Replicate: execute the exact same experiment with the same code and data • Reproduce: achieve the same results/conclusions with a different implementation • Difficult when datasets evolve, web data is involved, or when data is proprietary • Analyses often not well documented
- 33. Why replicate/reproduce? • To better understand each other’s work • To be able to build on each other’s work • Quality check • DHG will aim to make its research as reproducible as possible • We will aim to document all steps of the process (from data cleaning to visualisation) • Will make code and datasets available (where possible)
- 34. Sharing is caring See also: https://www.slideshare.net/Oorlogsbronnen/historicidagen-2017-collectieontsluiting-next-level-de-ijsberg-zichtbaar-maken
- 37. We need to learn each other’s language Humanities Language Technology Close reading Deep reading Coding Annotating Programming Coding Tool criticism Evaluation … ….
- 38. We need to learn about each others’ research methods Photo by DAVID ILIFF. License: CC-BY-SA 3.0 Image source: https://atos.net/content/dam/global/images/atos-cartesius-supercomputer-by-bull-copyright-surfsara.jpg
- 39. We need to look beyond our own domain Image source: https://pxhere.com/en/photo/994857 (It may not always be easy…)
- 41. Going forward • What questions would you like to answer with digital methods? • What awesome datasets/tools do you have? • How do you like your coffee? image source: http://www.independent.ie/incoming/article31308951.ece/ALTERNATES/h342/tea.jpg