Recommended
More Related Content
Similar to Counting Tales: Towards a Computational Narratology
Similar to Counting Tales: Towards a Computational Narratology (20)
Recently uploaded
Recently uploaded (20)
Counting Tales: Towards a Computational Narratology
- 2. A Pirate in a Tree
- 6. Rayne’s Location within Louisiana
- 7. • Population:8500. • 34%ofpopulationin2000wasAfricanAmerican. • AfricanAmericanstendtoliveindiscreteneighborhoods. • Blacksandwhitesinteractincommercialsectorsbutnotinresidentialareas. • AfricanAmericanresidentialareashaveanumberoffeaturesthatallowforthecreation andmaintenanceofadistinctfolkculture. Rayne, Louisiana (雷恩)
- 8. Oscar Babineaux • LivedinRaynehisentirelife. • Wellknown“shittalker.” • Talkingshitcaninclude: insults,jokes,andarhymedformofspeakingknownbyfolkloristsas “toasts”...andnowweknowitalsoincludeslegends. • Shittalkinghasmostlybeeninvestigatedasanurbanphenomenon,butitwouldappeartohavebeen activeinruralareassinceatleastthedaysofZoraNealeHurston’sMulesandMen.
- 9. Summary of Legend • Babineauxstopsbyhisfamilyhome. • Peoplearedigginginthebackyardfortreasure. • Joinsfamilyinprayer.. • Heandhisnephewtakewatertodiggers,encounterapirateinatree. • Pirateasksforsomethingtodrink. • Theygivehimsome. • Pirateasksagain. • Theygetscaredanddon’tgivehimanything. • Piratethreatensthem. • Theyrun.Ashovelfliesthroughtheairandlandsinatree.
- 10. Babineaux Text
- 11. Like I said my family was weird. They liked to dig for money and stuff. Said my grandfather had left us some money. And they was digging for it. So one day we went, and I was at work, so I can see, we at a country spot, like our property. So I can see a lot of people dressed in white. So I’m curious me. I said, “Well, shit, what the hell is everybody doing out there dressed in white? I wanna see.” So I goes out there. So they tell me, “You’re working right now, just go home come back. You know, come back after work.”
- 12. So I goes back, man, after work. So, they all in the house. We all praying man, everyone’s on their knees praying. They got an excavator in the back yard, digging. [Laughs.] You understand? Find this money, I guess. We’re on our knees, man, we’re praying. It’s like in the pit of the summer, like here. No wind nothing. They had a wind come through the house. That wind was so strong my aunt was holding onto the door like that, and both her legs was in the air. That’s how strong the wind was. In the house. So they said … they picked me, my nephew — the one I was telling you that talk all that shit, and my little niece to go bring some water to the workers in back, the one that was doing the work. So we got to walking. We passed on the side of the house to bring them.
- 13. So my nephew said, “Say man you see that guy in the tree?” I said, “Man fuck I don’t see nobody in no tree.” He said, “Yeah man he be right there sitting on that limb.” I said, “I don’t see nobody man.” I’m getting scared now. Man I don’t see nobody. But he’s seeing this, you know. So he said— I said, “How he look?” “It’s a guy,” he said, “it’s a guy dressed in a pirate suit, man.” He said, “He got a pirate hat on. He got a pirate jacket.” And he started talking to him. The guy in the tree started talking to him, while he’s telling me this. But the guy in the tree is telling him: shut up don’t tell me that.
- 14. So he telling me, “Man, look he right there. You can’t see him? Look he right there on that branch.” He say, “He want something more to drink.” You know, because what they had did: they’d put a bowl in the back yard, under this tree, with some alcohol in it. You understand? And I don’t know if it was the sun that would dissolve it, but it would be gone. Okay, so he say he say, “Man, he want another drink.” So I said, “Fuck man don’t tell me that .” I wanna get back in the house. I said, “I don’t see nobody up there.” So we kept on walking. We went out there. We brung them some water. So on our way back.
- 15. Look at him. He say, “See you, you son of a bitch.” He say, “You don’t wanna give me another drink, huh?” He say, “You gonna be just like me.” He say, “You see this here peg leg?” He say, “You going to be just like me.” He say, “For this out here y’all are going to have to lose something.” So, man, it got kind of scared. We started walking fast. By the time we got to the house, I broke out a run. A shovel, man, come from the back of the house. I mean full force.That shovel stuck in that tree so deep we had to dig it out with an axe. It stuck … you know with a shovel, it’s hard to stick a shovel into anything. That shovel went inside the tree halfway.
- 16. Method & Madness
- 17. 0 275 550 825 1100 LOH164 LOH165 LOH160 LAU14 LAU13 LOH157 LOH162 ANC88 LOH161 LOH159 LOH163 LOH162b LOH158 ANC91 ANC90 ANC89 Total Unique Figure 1: Graph of Length and Lexical Diversity of Oral Legend Texts
- 18. TEXT TOTALWORDS UNIQUEWORDS PERCENTAGE ANC88 331 142 0.43 ANC89 153 83 0.54 ANC90 175 86 0.49 ANC91 176 105 0.60 BRO-01 117 74 0.63 BRO-02 67 50 0.75 BRO-03 136 90 0.66 BRO-04 122 79 0.65 LAU13 375 175 0.47 LAU14 655 207 0.32 LOH157 364 166 0.46 LOH158 193 106 0.55 LOH159 282 144 0.51 LOH160 761 287 0.38 LOH161 295 129 0.44 LOH162 332 144 0.43 LOH162b 194 108 0.56 LOH163 209 114 0.55 LOH164 1025 318 0.31 LOH165 905 277 0.31 Details of Lexical Diversity of Oral Texts
- 19. 0 50 100 150 200 morris-02 goble-03 goble-04 davis-02 morris-01 goble-05 davis-03 goble-01 goble-02 davis-01 bridgwaters-01 Total Unique Figure 1: Graph of Length and Lexical Diversity of Oral History Texts
- 20. 0 250 500 750 1000 1250 1500 LOH 164 LOH 160 LAU 13 LOH 162 LOH 161 LOH 163 LOH 162b ANC 91 goble-03 goble-04 BRO-04 davis-02 goble-05 goble-01 davis-01 bridgwaters-01 TotalWordsforLegend UniqueWordsforLegend TotalWordsforOralHistory UniqueWordsforOralHistory
- 22. Most Frequent Words in Legends the 251 and 202 was 201 he 191 it 175 to 154 a 143 they 135 that 121 i 121 there 105 of 102 in 100 said 77 you 67 had 66 this 63 so 63 on 48 but 47 me 46 went 45 out 44 him 42 well 40 we 40 up 40 money 40 back 39 one 38 man 38 got 36 his 34 all 34 with 33 where 33 just 33 know 32 see 29 when 28 them 28 like 28 go 27 told 26 she 26 little 26 what 25 old 25 now 25 my 24 here 23 some 22 about 22 time 21 over 21 come 21 at 21 something 20 house 19
- 23. LEXICALDIVERSITYLEXICALDIVERSITYLEXICALDIVERSITY LENGTHLENGTHLENGTH COLLECTION AVERAGE MINIMUM MAXIMUM AVERAGE MININUM MAXIMUM Legends 0.46 0.31 0.60 (0.75) 343 (67) 153 1025 Oral Histories 0.65 0.55 0.79 111 43 196 Comparison of Lengths and Lexical Diversity
- 26. Why Count Words? (Redux) •Wecanassumethatallgenreswillhaveawidevarietyofforms,but,lookedat fromastatisticalperspective,aretheresomepatternstherethatmightleadus tomoreinterestinginvestigations? •Willfolktalesemergeastypicallylonger,andwillthatlengthbeafunctionof thenecessityofsettingupamorecomplexstoryworld? •E.g.,RayHicks’tellingof“JackandtheFireDragon”is1977words.(See handout.) •Whataboutlexicaldiversityandfrequencies? •Andhowdothesesimplemattersofcountingrelatetomorphologiesand semanticnetworks?
- 27. After We Count Words... • Isaidearlierthat“inoraldiscourse,everywordmatters.”That’snotentirelytrue. • Aswesawintheprevioussetsofstatistics,therearealotofwordsthatdon’tmattervery muchwhenwethinkaboutthemeaningofastory: the 251 and 202 was 201 he 191 it 175 to 154 a 143 they 135 that 121 i 121 there 105 of 102 in 100 said 77 you 67 had 66 this 63 so 63 on 48 but 47 me 46 went 45 out 44 him 42 well 40 we 40 up 40 money 40 back 39 one 38 man 38 got 36 his 34 all 34 with 33 where 33 just 33 know 32 see 29
- 29. Content Words in Context g to see . BA : There was n't money buried there ? SF : Supposedly. That 's wh yed around his grave a lot . He was buried , still buried , where we lived . H grave a lot . He was buried , still buried , where we lived . He was buried in ll buried , where we lived . He was buried in the yard where I lived . They ha m . Killed him . His wife and Billy buried him right there . That night as it ppi . That night , supposedly , she buried her money on the other side of Jean nd early American coins . They were buried there . They said it was Lafitte . West was the outlaw that did that ( buried the money ) , according to the Mexi s told the story that this money is buried in there. By the time they made the n't remember where it was. They had buried all this money he had. What it was tly , this money was supposed to be buried there. That money , as far as anybo re 's always been a claim that they buried whatever they had somewhere in that ime ago ( pause ) they claimed they buried their money. That was back when the was seven-foot deep. You could 've buried a car in it. There was some of them hat , well usually treasure , money buried around. These three fellows came up ese treasure things, like you hunt buried treasure with. He had one of those
- 35. What Python looks like #! /usr/bin/env python import glob import re files = {} for fpath in glob.glob("*.txt"): with open(fpath) as f: fixed_text = re.sub("[^a-zA-Z'-]"," ",f.read()) files[fpath] = (len(fixed_text.split()),len(set(fixed_text.split()))) print "Total Words:" , len(fixed_text.split()) print "Total Unique:",len(set(fixed_text.split())) with open("wordstats.csv", "w") as f: for fname in files: print >> f , "%s,%s,%s"%(fname,files[fname][0],files[fname][1])
- 36. That doesn’t look like folklore studies...
- 37. Neither did Propp or Lévi- Strauss...
- 41. So what?
- 43. A Future for Folklore Studies? •Performancetheory/ethnomethodologies/ethnographyofspeakinggiveussome ofthemostaccurateaccountsofhumanverbalbehaviorintheworld. •Lord’sworkonoralformulasisnowfoundationalworkincognitivescience. See:DavidRubin’sMemoryinOralTradition.(Samplechapterisinpacketof papers.) •Alsoinpacket:computerscientistsworkingonmorphology,physicistsmapping ofmyth. Sorryforletter-sizedpages:PDFswillbeinDropboxfolder.