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Why language technology can’t handle Game of Thrones (yet)
The document discusses the challenges in applying natural language processing (NLP) to modern fantasy literature, particularly analyzing named entities within texts like 'Game of Thrones.' It outlines a study comparing named entity recognition performance on 20 older classical novels to 20 newer science fiction and fantasy novels, detailing methodologies, tools employed, and preprocessing steps. The current capabilities of NLP technologies and their limitations in accurately processing complex fictional narratives are highlighted.
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Why language technology can’t handle Game of Thrones (yet)
- 1. Why Language TechnologyCan’t Handle Game of Thrones (yet) Marieke van Erp merpeltje Joint work with: Niels Dekker & Tobias Kuhn Image source: https://anibundel.files.wordpress.com/2015/04/jonsnow-leaves-ygritte.jpg
- 2. This talk • NLP101 • Recognising named entities in fiction • Digital Humanities @KNAW HuC D I G I TA L H U M A N I T I E S L A B Image source: https://vignette.wikia.nocookie.net/pirates/images/3/3c/ MediterraneanProfile.jpg/revision/latest?cb=20120312215230
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- 5. NLP 101 Image source:https://i.ytimg.com/vi/iuumnjJWFO4/maxresdefault.jpg
- 6. NLP 101: Whatis Text Mining? • Extracting knowledge and information from texts in natural language: • metadata for a text: author, publisher, time of publication, topic, its language, URL, URLs to and from a web text • people mentioned in text, but also companies, organisations, places, dates → links to Wikipedia, Wikification of text • Amounts: prices, age, size, distance, weight • Facts (statements), concepts (terms) and relations between concepts • Sentiment (positive/negative), opinions • Emotions, purpose, intention, humour, sarcasm, irony, threats, style (formal, informal), genre (blog, news, science, tax form)
- 7. Types of Knowledgeto Extract • Conceptual relations: define possible relations between concepts in an ontology, e.g. what things have weight, size, age, get born, eat, drink, get an education, work, marry, do sports, live and die. • Factual relations: actual instantiations of concepts and relations that are the case in some world (time and place), Barack Obama was born on Augus 4, 1961, in Honolulu, Hawaii. • Factual relations need to fit the ontological model but the ontology does not predict actual facts only the possible facts!!! • Opinions: epistemic and modal relations (believe, wish, hope, fear, expect) between source and target expressed as a private state of the source, e.g. I am a fan of Barack Obama, I believe Barack Obama will help people.
- 10. Text Mining pipeline •Analysis starts at token-level • Moves up to phrases, sentences and documents • Performance goes down as analyses becomes deeper • Statistical methods mostly used, but hybrid methods are a promising research topic Tokenisation Lexical Analysis Syntactic Analysis Semantic Analysis Pragmatic Analysis Input text Speaker's intended meaning
- 11. Companies want textmining • From click logs they can see what people looked at on their site • To know what they think about it they need to mine reviews, tweets etc: text mining • To stay ahead of their competitors, they need to obfuscate their patents, and find relevant patents from competitors: text mining • To aid their information departments, they need access to relevant information: text mining
- 12. Humanities researchers wanttext mining • To evaluate gender bias in large corpora http:// literaryquality.huygens.knaw.nl/ • To trace concepts through time: https://www.esciencecenter.nl/project/ evidence • Detecting and modelling populist movements on social media https:// www.meertens.knaw.nl/cms/en/research/projects/259-het-dagelijks-leven/ 145541-populisme-social-media-en-religie • Analysis of church registers, letters, ship journals etc…
- 13. State-of-the-art • POS tagging:97% • Sentiment Analysis: 95% (document level) / 54% (fine-grained sentence level) • Named Entity Recognition: 90% • Temporal information extraction: 77% Note: this holds for English and on standardised datasets
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- 16. Recognising named entitiesin fiction Image source: https://wp-media.patheos.com/blogs/sites/1186/2019/04/mauricio-santos-503880-unsplash.jpg
- 17. Background • Characters andrelations are backbone of stories • Computational methods allow for scaling up network extraction and analysis • Relies on named entity recognition • Most work thusfar focuses on 19th and early 20th century novels • Research question: how do these tools perform on modern science fiction/fantasy novels? D I G I TA L H U M A N I T I E S L A B Image source: https://newleftreview.org/system/dragonfly/production/2019/03/09/9rcllsj7us_3020501.gif
- 18. Experimental setup • Collect20 ‘old’ and 20 ‘new’ novels • Annotate first chapters for entities and relationships between entities (gold standard) • Run 4 named entity recognisers on the sets of ‘old’ and ‘new’ novels • Compare system outputs to gold standard annotations • Bonus: compare network structures Image source: delpher.nl D I G I TA L H U M A N I T I E S L A B Image source: https://cdn-images-1.medium.com/max/2400/1*QbCo9uE7jPbt1ttnMsqOog.jpeg
- 19. 19th and early20th century novels, based on The Guardian’s Top 100 Classic novels + availability through Project Gutenberg + used in earlier studies
- 20. ‘New’ Science Fictionand Fantasy novels, based on list from BestFantasyBooks.com
- 21. Data preprocessing • Allbooks converted to plain text format • Ensure all texts have the same character encoding • Pro tip: check whether there are no odd or inconsistent quotation marks in your documents • Appendices, glossaries and reviews were removed manually D I G I TA L H U M A N I T I E S L A B Image source: https://www.dataentryoutsourced.com/blog/wp-content/uploads/2015/03/ Post-091-640x200.jpg
- 22. Gold standard annotations •Chapter lengths varied from 84 to 1,442 sentences • An average of 300 sentences close to a chapter boundary was selected • e.g. the third chapter in Alice in Wonderland ended after sentence 315, so for that book the first three chapters were annotated • 2 annotators (not the authors of the study) D I G I TA L H U M A N I T I E S L A B Image source: https://panmacmillan.azureedge.net/pmk11/panmacmillan/files/media/ panmacmillan/blogs/tws/august%202017/alice-in-wonderland-knowledge-quiz-header.png
- 23. Annotation Instructions • Foreach sentence: • Identify all characters in it • Identify anaphoric references (e.g. she refers to Alice) • To speed up the process, annotators were provided with a list of characters derived automatically • Missing characters could be added to the list • Ignore generic pronouns, exclamations, generic noun phrases, non-human named characters (Buckbeak) D I G I TA L H U M A N I T I E S L A B Image source: https://vignette.wikia.nocookie.net/p__/images/3/35/Erich_Mueller_and_Shannon_McGrath_are_glued_together_back_to_back_with_Tree_Resin.jpeg/revision/ latest?cb=20170331180847&path-prefix=protagonist
- 24. Named Entity Recognisers: BookNLP •NLP pipeline modified to deal with books • POS tagging, dependency parsing, NER, character name clustering, quotation speaker identification, pronominal coreference resolution, supersense tagging • NER module based on Stanford NER, with some modifications • We focus on NER, character name clustering and pronominal character resolution modules in our evaluation • https://github.com/dbamman/book-nlp D I G I TA L H U M A N I T I E S L A B Image source: https://cdn.aarp.net/content/dam/aarp/money/budgeting_savings/2016/04/1140- yeager-sell-your-used-books.imgcache.rev6feda141288df73e8fd100822bb375ea.jpg
- 25. Named Entity Recognisers: StanfordNER • State-of-the-art CRF NER system • Trained on CoNLL 2003 data (Reuters newswire articles from 1996-08-20 to 1997-08-19) • Cited 2,720 times • F1 = 86.31 on CoNLL 2003 test set • https://nlp.stanford.edu/software/CRF- NER.html D I G I TA L H U M A N I T I E S L A B
- 26. Named Entity Recognisers: IllinoisTagger • Perceptron-based classifier • Includes contextual information • 10,146 downloads • F1 = 90.57 on CoNLL 2003 test set • https://cogcomp.org/page/software_view/ NETagger Image source: delpher.nl D I G I TA L H U M A N I T I E S L A B
- 27. Named Entity Recognisers: IXA-Pipe-NERC •Perceptron model • additional background information from Brown clusters • F1 = 91.36 on CoNLL 2003 test • https://github.com/ixa-ehu/ixa-pipe-nerc D I G I TA L H U M A N I T I E S L A B
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- 39. Discussion • No differencebetween ‘old’ and ‘new’ books • Within categories, great variety in entity distributions and results • If a central entity is missed, the performance suffers greatly (e.g. Brave New World) • Coreference resolution particularly difficult in this domain D I G I TA L H U M A N I T I E S L A B Image source: https://www.nuffoodsspectrum.in/uploads/articles/quarterly_results_bg-4192.jpg
- 40. Why is fictionhard for NLP? • Fiction writers don’t have to abide by conventions: they can use language more creatively than newspaper journalists • mix languages • make up languages • use nicknames • Narratives written from first-person perspective confuse the software D I G I TA L H U M A N I T I E S L A B Image source: https://steamuserimages-a.akamaihd.net/ugc/859477733475369907/F34770D6EFEC30A70A84BEFE93C2C522C0B4A902/
- 41. ChalaisChalais M. BonacieuxM. Bonacieux deM. 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 Grimaud MonsieurGrimaud 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 Medicisde Marie Medicis 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 The Three Musketeers: F1 32 - 48
- 42. ChalaisChalais M. BonacieuxM. Bonacieux deM. 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... The Three Musketeers after rewriting d’Artagnan to Dartagnan
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- 44. Performance fixes • Replaceword names with generic names • Remove apostrophes from names • But: • Requires manual intervention • Doesn’t scale D I G I TA L H U M A N I T I E S L A B
- 45. Where to gofrom here? • Robuster NLP tools are necessary to better understand novels (and other non-newspaper texts) • Background knowledge can help (e.g. GoT Wiki lists all Danaerys’ nicknames) • But: not all books are that popular • Also: different names are used in different contexts, you may not want to collapse them! • Always: don’t just assume it works, look into your data! • Full paper at: http://peerj.com/articles/cs-189 D I G I TA L H U M A N I T I E S L A B Image source: https://news.images.itv.com/image/file/1232718/stream_img.jpg
- 46. Digital Humanities Lab History, LiteraryStudies, History of Science & Scholarship Social History Dutch Language & Culture https://huc.knaw.nl/
- 47. Slide by Antalvan den Bosch
- 48. Slide by Antalvan den Bosch
- 49. Cultural Artificial Intelligence MakingAI culturally aware Appreciate the user Being contextually appropriate Understand the issues What do you get when you invert “Digital Humanities”? Slide by Antal van den Bosch
- 50. Applications of CulturalAI: Filters and flags • Toxicity • Protective filters (like spam filters and ad blockers) • Gender • Linguistic filters and helpers • Fake news • Meme detectors, explanations Slide by Antal van den Bosch
- 51. Theory of CulturalAI: Understanding & nuance • Understanding concepts • Changes over time • Perspectives • Evolution • Knowing the origins of digital stories • Understanding viral potential • Language is “social and cultural data” (Nguyen, 2017) Slide by Antal van den Bosch
- 52. Some DHLab projects •Food culture via newspaper recipes (Meertens and IISH) • Analysing online debates: refugee vs migrant (with EUR) • Amsterdam Time Machine (with many partners) • Tracing 18th century career trajectories (with HuC-DI & Huygens Institute) • Analysing the concept ‘violence’ through time (with NLeSc, OU & NIOD) D I G I TA L H U M A N I T I E S L A B
- 53. Debates on therefugee crisis • From 2015 on, wider use of both ‘European refugee crisis’ and ‘European migrant crisis’ in the news and social media • “Framing labels” (Knoll, Redlawsk, & Sanborn, 2011) imply two different frames: • ‘Refugee’ – people fleeing conflict or persecution • ‘Migrant’ – improving economic situation • Mixed usage and mislabeling have implications for refugees, e.g., negative influence on perceptions of host countries D I G I TA L H U M A N I T I E S L A B
- 55. DHLab@HuC: Advancing the humanitiesthrough digital methods • DHLabHuC / adinanerghes / melvinwevers / merpeltje • https://dhlab.nl (under construction) Melvin WeversAdina NerghesMarieke van Erp
- 56. Grazie per lavostra attenzione!