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LaTeCH 2015: Measuring the Structural and Conceptual Similarity of Folktales using Plot Graphs (Lestari & Manurung)

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Ruli Manurung
Ruli Manurung

狠狠撸s from my talk at LaTeCH 2015 (The 9th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, held in conjunction with ACL-IJCNLP 2015) in Beijing, 30 July 2015. The full paper can be found in the ACL Anthology at http://aclweb.org/anthology/W/W15/W15-3704.pdf

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Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Measuring the Structural and Conceptual Similarity of Folktales using Plot Graphs Victoria Anugrah Lestari & Ruli Manurung Faculty of Computer Science Universitas Indonesia victoria.anugrah@ui.ac.id, maruli@cs.ui.ac.id Beijing, China 30 July 2015
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Folktales Folktales are a characteristically anonymous, timeless, and placeless tale circulated orally among a people. http://onceuponatime.wikia.com/wiki/Rumpelstiltskin_(Fairytale) http://indonesianfolklore.blogspot.com/2007/10/lutung-kasarung-folklore-from-west-java.html http://indonesianfolklore.blogspot.com/2007/10/keong-emas-golden-snail-prince-raden.html 2/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Humanities work on folktales ? Vladimir Propp (1928): Morphology of the (Russian) folktale ? story grammars ? Aarne-Thompson-Uther (ATU) index (1910, 1961, 2004): story motifs, hierarchy of folktale types 3/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Computational work on folktales ? Vaz Lobo & de Matos (2010): latent semantic mapping + clustering 453 fairy tales from Gutenberg. ? Nguyen et al. (2012): classification based on genre, e.g. legend, fairytale, jokes, puzzle, urban legend, etc. using lexical, POS, NE, metadata. ? Nguyen et al. (2013): Ranking based on story types (ATU, Brunvand) using IR, lexical, SVO triplets. ? Karsdorp & van den Bosch (2013): Topic modelling (L-LDA) for multiple labelling of ATU motifs (defined by types). 4/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Folktales as narratives ? Narratives: Focus on sequence of related events ? structure ? Models of narrative: Turner (1994), Mateas & Stern (2003), Pérez y Pérez & Sharples (2004), etc. 5/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Folktales as narratives ? Narratives: Focus on sequence of related events ? structure ? Models of narrative: Turner (1994), Mateas & Stern (2003), Pérez y Pérez & Sharples (2004), etc. ? However: Fisseni & L?we (2012): People tend to focus on motifs & content, less on structure. 5/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Plot graphs (McIntyre & Lapata, 2010) 6/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Goals of this work ? Construct representations that capture structural & conceptual properties. ? Define similarity metric, use to organize folktales. ? Compare to BoW-based methods wrt. ATU. 7/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Representing folktales as plot graphs 8/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Representing folktales as plot graphs Action nodes: Action edges: 8/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Representing folktales as plot graphs Action nodes: Child nodes: Action edges: Action- Child edges: 8/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Representing folktales as plot graphs Action nodes: Child nodes: Entity nodes: Action edges: Action- Child edges: Entity edges: 8/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Representing folktales as plot graphs Note that the core structure is linear. Action nodes: Child nodes: Entity nodes: Action edges: Action- Child edges: Entity edges: 8/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Example 9/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Example live lion forest subj in 9/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Example live sleep lion forest it tree subj in subj under 9/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Example live sleep come lion forest it tree mouse subj in subj under subj 9/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Example live sleep come play lion forest it tree mouse lionit subj in subj under subj subj on 9/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Automatic construction Stanford CoreNLP SemanticGraph (a.k.a. dependency parse) 10/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments From SemanticGraph to plot graph Some observation-based heuristics on selecting relations: ? Governors of nsubj (nominal subject), expl (expletive “there”), and aux (auxiliary) ? Add child if relation(parent,child) not conj, comp, adv, aux, cop, dep, expl, mark 11/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Construction example 12/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Construction example CoreNLP CorefChain (length >1) 12/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Construction example CoreNLP CorefChain (length >1) 12/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Construction example CoreNLP CorefChain (length >1) 12/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Final result 13/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Measuring plot graph similarity A lion lives in the forest. One day it sleeps under a tree. Then a mouse plays on the lion and disturbs its sleep. A lion eats meat. A lion lives in the jungle. One day it rests under a tree. 14/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Measuring plot graph similarity A lion lives in the forest. One day it sleeps under a tree. Then a mouse plays on the lion and disturbs its sleep. A lion eats meat. A lion lives in the jungle. One day it rests under a tree. 14/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Alignment of event sequence Needleman- Wunsch 15/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Conceptual similarity: Wu-Palmer Measure path distance between 2 words based on WordNet taxonomy Word pairs Similarity sleep, live 0.25 disturb, rest 0.33 live, eat 0.29 prince, king 0.94 jungle, forest 0.31 palace, house 0.91 16/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Example mapping eat live rest live 0.29 1 0.33 sleep 0.22 0.25 0.43 play 0.29 0.33 0.43 disturb 0.29 0.33 0.33 eat live rest 0 -1 -2 -3 live -1 0.29 0 -1 sleep -2 -0.71 0.54 1 play -3 -1.71 -0.38 0.96 disturb -4 -2.71 -1.38 -0.04 Wu-Palmer similarity Alignment scoring & traceback matrix 17/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Folktale similarity measurement p1 & p2 = the two plot graphs being compared α = weighting for action node similarity β = weighting for child node similarity (a1i ,a2i ) = pair of action nodes from alignment of p1 and p2 g = gap penalty (c1i ,c2i ) = pair of child nodes from alignment of p1 and p2 n = alignment length of p1 and p2 18/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Initial experiment ? Determining values for α, β, and g ? For each story, 5 paraphrases manually created: word replacement, sentence structure change, insertion/deletion of phrases & sentences ? Measure similarity between paraphrases & across stories. Maximize difference. No. Title #Words 1 A friend in need is a friend indeed 133 2 Honesty is the best policy 129 3 A town mouse and a country mouse 260 4 How to tell a true princess 382 5 The butterfly lovers 572 6 Rumpelstiltskin 1106 http://www.english-for-students.com/Simple-Short-Stories.html 19/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Similarity scores using various parameters g= α = 0.7, β = 0.3 α = 0.5, β = 0.5 α = 0.3, β = 0.7 -1 -0.5 0 -1 -0.5 0 -1 -0.5 0 Between paraphrases Avg 0.83 0.80 0.74 0.83 0.80 0.73 0.83 0.79 0.71 Min 0.69 0.61 0.53 0.69 0.60 0.49 0.68 0.58 0.45 Across stories Avg 0.37 0.30 0.15 0.41 0.32 0.12 0.45 0.33 0.09 Max 0.55 0.45 0.25 0.55 0.43 0.20 0.55 0.42 0.16 BP min - AS max 0.14 0.16 0.28 0.14 0.17 0.29 0.13 0.16 0.29 Diff. between avgs 0.46 0.50 0.59 0.42 0.48 0.61 0.38 0.46 0.62 20/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Main experiment: BoW comparison 24 fairy tales from Fairy Books of Andrew Lang, grouped into 5 clusters under ATU (fairy tales): ? Supernatural Adversaries — Bluebeard; Hansel and Gretel; Jack and the Beanstalk; Rapunzel; The Twelve Dancing Princesses. ? Supernatural or Enchanted Relatives — Beauty and the Beast; Brother and Sister; East of the Sun, West of the Moon; Snow White and Rose Red; The Bushy Bride; The Six Swans; The Sleeping Beauty. ? Supernatural Helpers — Cinderella; Donkey Skin; Puss in Boots; Rumpelstiltskin; The Goose Girl; The Story of Sigurd. ? Magic Objects — Aladdin and the Wonderful Lamp; Fortunatus and His Purse; The Golden Goose; The Magic Ring. ? Other Stories of the Supernatural — Little Thumb; The Princess and the Pea. Measure similarity between clusters & across clusters. http://www.gutenberg.org/ebooks/30580 21/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity ExperimentsStory type Story Plot graph Bag of words Combination Within Across Within Across Within Across Supernatural adversaries Bluebeard 0.1000 0.1037 0.8629 0.8618 0.4814 0.4586 Hansel and Gretel 0.1075 0.1157 0.8492 0.8630 0.4783 0.4894 Jack and the Beanstalk 0.1050 0.1110 0.9050 0.8891 0.5050 0.5001 Rapunzel 0.1000 0.1047 0.8790 0.8575 0.4895 0.4571 The Twelve Dancing Princesses 0.1125 0.1073 0.8808 0.8631 0.4966 0.4610 Supernatural or enchanted relatives Beauty and the Beast 0.0767 0.0705 0.8803 0.8605 0.4785 0.4397 Brother and Sister 0.1233 0.1135 0.8881 0.8722 0.5057 0.4654 East of the Sun, West of the Moon 0.1117 0.1012 0.8914 0.8571 0.5015 0.4525 Snow White and Rose Red 0.1200 0.1165 0.8650 0.8566 0.4925 0.4865 The Bushy Bride 0.1200 0.1182 0.8862 0.8739 0.5031 0.4960 The Six Swans 0.0925 0.1100 0.9006 0.8662 0.5020 0.4881 The Sleeping Beauty 0.1125 0.1194 0.8990 0.8918 0.5087 0.5056 Supernatural helpers Cinderella 0.1180 0.1144 0.8150 0.8306 0.4665 0.4725 Donkey Skin 0.1040 0.1122 0.8873 0.9025 0.4956 0.5074 Puss in Boots 0.1175 0.1095 0.8170 0.8486 0.4672 0.4551 Rumpelstiltskin 0.0750 0.0858 0.8467 0.8569 0.4609 0.4478 The Goose Girl 0.1240 0.1178 0.8617 0.8624 0.4928 0.4643 The Story of Sigurd 0.1080 0.1178 0.8516 0.8670 0.4800 0.4664 Magic objects Aladdin and the Wonderful Lamp 0.0975 0.0910 0.8958 0.8664 0.4946 0.4559 Fortunatus and His Purse 0.1133 0.1185 0.8945 0.8306 0.5039 0.4519 The Golden Goose 0.1033 0.1155 0.9006 0.8529 0.5012 0.4611 The Magic Ring 0.1033 0.1040 0.9120 0.8960 0.5077 0.4762 Other stories Little Thumb 0.0300 0.1214 0.7444 0.8562 0.3872 0.4675 The Princess and the Pea 0.0300 0.0405 0.7444 0.7844 0.3872 0.3945 # Similarity within > across 10 (41.67%) 15 (62.50%) 19 (79.16%)
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Analysis & Discussion ? Errors in automatic construction (dependency parses aren’t really semantic graphs), e.g.: “along came a mouse” vs. “a mouse came”, coreference errors. ? Consistent with Fisseni & L?we (2012) findings: focus more on content & motifs? ? Combination of plot graph + BoW yields best results. 23/24
Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments ? THANK YOU ? 24/24

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LaTeCH 2015: Measuring the Structural and Conceptual Similarity of Folktales using Plot Graphs (Lestari & Manurung)

  • 1. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Measuring the Structural and Conceptual Similarity of Folktales using Plot Graphs Victoria Anugrah Lestari & Ruli Manurung Faculty of Computer Science Universitas Indonesia victoria.anugrah@ui.ac.id, maruli@cs.ui.ac.id Beijing, China 30 July 2015
  • 2. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Folktales Folktales are a characteristically anonymous, timeless, and placeless tale circulated orally among a people. http://onceuponatime.wikia.com/wiki/Rumpelstiltskin_(Fairytale) http://indonesianfolklore.blogspot.com/2007/10/lutung-kasarung-folklore-from-west-java.html http://indonesianfolklore.blogspot.com/2007/10/keong-emas-golden-snail-prince-raden.html 2/24
  • 3. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Humanities work on folktales ? Vladimir Propp (1928): Morphology of the (Russian) folktale ? story grammars ? Aarne-Thompson-Uther (ATU) index (1910, 1961, 2004): story motifs, hierarchy of folktale types 3/24
  • 4. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Computational work on folktales ? Vaz Lobo & de Matos (2010): latent semantic mapping + clustering 453 fairy tales from Gutenberg. ? Nguyen et al. (2012): classification based on genre, e.g. legend, fairytale, jokes, puzzle, urban legend, etc. using lexical, POS, NE, metadata. ? Nguyen et al. (2013): Ranking based on story types (ATU, Brunvand) using IR, lexical, SVO triplets. ? Karsdorp & van den Bosch (2013): Topic modelling (L-LDA) for multiple labelling of ATU motifs (defined by types). 4/24
  • 5. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Folktales as narratives ? Narratives: Focus on sequence of related events ? structure ? Models of narrative: Turner (1994), Mateas & Stern (2003), Pérez y Pérez & Sharples (2004), etc. 5/24
  • 6. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Folktales as narratives ? Narratives: Focus on sequence of related events ? structure ? Models of narrative: Turner (1994), Mateas & Stern (2003), Pérez y Pérez & Sharples (2004), etc. ? However: Fisseni & L?we (2012): People tend to focus on motifs & content, less on structure. 5/24
  • 7. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Plot graphs (McIntyre & Lapata, 2010) 6/24
  • 8. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Goals of this work ? Construct representations that capture structural & conceptual properties. ? Define similarity metric, use to organize folktales. ? Compare to BoW-based methods wrt. ATU. 7/24
  • 9. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Representing folktales as plot graphs 8/24
  • 10. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Representing folktales as plot graphs Action nodes: Action edges: 8/24
  • 11. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Representing folktales as plot graphs Action nodes: Child nodes: Action edges: Action- Child edges: 8/24
  • 12. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Representing folktales as plot graphs Action nodes: Child nodes: Entity nodes: Action edges: Action- Child edges: Entity edges: 8/24
  • 13. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Representing folktales as plot graphs Note that the core structure is linear. Action nodes: Child nodes: Entity nodes: Action edges: Action- Child edges: Entity edges: 8/24
  • 14. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Example 9/24
  • 15. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Example live lion forest subj in 9/24
  • 16. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Example live sleep lion forest it tree subj in subj under 9/24
  • 17. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Example live sleep come lion forest it tree mouse subj in subj under subj 9/24
  • 18. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Example live sleep come play lion forest it tree mouse lionit subj in subj under subj subj on 9/24
  • 19. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Automatic construction Stanford CoreNLP SemanticGraph (a.k.a. dependency parse) 10/24
  • 20. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments From SemanticGraph to plot graph Some observation-based heuristics on selecting relations: ? Governors of nsubj (nominal subject), expl (expletive “there”), and aux (auxiliary) ? Add child if relation(parent,child) not conj, comp, adv, aux, cop, dep, expl, mark 11/24
  • 21. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Construction example 12/24
  • 22. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Construction example CoreNLP CorefChain (length >1) 12/24
  • 23. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Construction example CoreNLP CorefChain (length >1) 12/24
  • 24. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Construction example CoreNLP CorefChain (length >1) 12/24
  • 25. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Final result 13/24
  • 26. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Measuring plot graph similarity A lion lives in the forest. One day it sleeps under a tree. Then a mouse plays on the lion and disturbs its sleep. A lion eats meat. A lion lives in the jungle. One day it rests under a tree. 14/24
  • 27. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Measuring plot graph similarity A lion lives in the forest. One day it sleeps under a tree. Then a mouse plays on the lion and disturbs its sleep. A lion eats meat. A lion lives in the jungle. One day it rests under a tree. 14/24
  • 28. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Alignment of event sequence Needleman- Wunsch 15/24
  • 29. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Conceptual similarity: Wu-Palmer Measure path distance between 2 words based on WordNet taxonomy Word pairs Similarity sleep, live 0.25 disturb, rest 0.33 live, eat 0.29 prince, king 0.94 jungle, forest 0.31 palace, house 0.91 16/24
  • 30. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Example mapping eat live rest live 0.29 1 0.33 sleep 0.22 0.25 0.43 play 0.29 0.33 0.43 disturb 0.29 0.33 0.33 eat live rest 0 -1 -2 -3 live -1 0.29 0 -1 sleep -2 -0.71 0.54 1 play -3 -1.71 -0.38 0.96 disturb -4 -2.71 -1.38 -0.04 Wu-Palmer similarity Alignment scoring & traceback matrix 17/24
  • 31. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Folktale similarity measurement p1 & p2 = the two plot graphs being compared α = weighting for action node similarity β = weighting for child node similarity (a1i ,a2i ) = pair of action nodes from alignment of p1 and p2 g = gap penalty (c1i ,c2i ) = pair of child nodes from alignment of p1 and p2 n = alignment length of p1 and p2 18/24
  • 32. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Initial experiment ? Determining values for α, β, and g ? For each story, 5 paraphrases manually created: word replacement, sentence structure change, insertion/deletion of phrases & sentences ? Measure similarity between paraphrases & across stories. Maximize difference. No. Title #Words 1 A friend in need is a friend indeed 133 2 Honesty is the best policy 129 3 A town mouse and a country mouse 260 4 How to tell a true princess 382 5 The butterfly lovers 572 6 Rumpelstiltskin 1106 http://www.english-for-students.com/Simple-Short-Stories.html 19/24
  • 33. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Similarity scores using various parameters g= α = 0.7, β = 0.3 α = 0.5, β = 0.5 α = 0.3, β = 0.7 -1 -0.5 0 -1 -0.5 0 -1 -0.5 0 Between paraphrases Avg 0.83 0.80 0.74 0.83 0.80 0.73 0.83 0.79 0.71 Min 0.69 0.61 0.53 0.69 0.60 0.49 0.68 0.58 0.45 Across stories Avg 0.37 0.30 0.15 0.41 0.32 0.12 0.45 0.33 0.09 Max 0.55 0.45 0.25 0.55 0.43 0.20 0.55 0.42 0.16 BP min - AS max 0.14 0.16 0.28 0.14 0.17 0.29 0.13 0.16 0.29 Diff. between avgs 0.46 0.50 0.59 0.42 0.48 0.61 0.38 0.46 0.62 20/24
  • 34. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Main experiment: BoW comparison 24 fairy tales from Fairy Books of Andrew Lang, grouped into 5 clusters under ATU (fairy tales): ? Supernatural Adversaries — Bluebeard; Hansel and Gretel; Jack and the Beanstalk; Rapunzel; The Twelve Dancing Princesses. ? Supernatural or Enchanted Relatives — Beauty and the Beast; Brother and Sister; East of the Sun, West of the Moon; Snow White and Rose Red; The Bushy Bride; The Six Swans; The Sleeping Beauty. ? Supernatural Helpers — Cinderella; Donkey Skin; Puss in Boots; Rumpelstiltskin; The Goose Girl; The Story of Sigurd. ? Magic Objects — Aladdin and the Wonderful Lamp; Fortunatus and His Purse; The Golden Goose; The Magic Ring. ? Other Stories of the Supernatural — Little Thumb; The Princess and the Pea. Measure similarity between clusters & across clusters. http://www.gutenberg.org/ebooks/30580 21/24
  • 35. Beijing 30 July ‘15 Folktales Plot graphs Similarity ExperimentsStory type Story Plot graph Bag of words Combination Within Across Within Across Within Across Supernatural adversaries Bluebeard 0.1000 0.1037 0.8629 0.8618 0.4814 0.4586 Hansel and Gretel 0.1075 0.1157 0.8492 0.8630 0.4783 0.4894 Jack and the Beanstalk 0.1050 0.1110 0.9050 0.8891 0.5050 0.5001 Rapunzel 0.1000 0.1047 0.8790 0.8575 0.4895 0.4571 The Twelve Dancing Princesses 0.1125 0.1073 0.8808 0.8631 0.4966 0.4610 Supernatural or enchanted relatives Beauty and the Beast 0.0767 0.0705 0.8803 0.8605 0.4785 0.4397 Brother and Sister 0.1233 0.1135 0.8881 0.8722 0.5057 0.4654 East of the Sun, West of the Moon 0.1117 0.1012 0.8914 0.8571 0.5015 0.4525 Snow White and Rose Red 0.1200 0.1165 0.8650 0.8566 0.4925 0.4865 The Bushy Bride 0.1200 0.1182 0.8862 0.8739 0.5031 0.4960 The Six Swans 0.0925 0.1100 0.9006 0.8662 0.5020 0.4881 The Sleeping Beauty 0.1125 0.1194 0.8990 0.8918 0.5087 0.5056 Supernatural helpers Cinderella 0.1180 0.1144 0.8150 0.8306 0.4665 0.4725 Donkey Skin 0.1040 0.1122 0.8873 0.9025 0.4956 0.5074 Puss in Boots 0.1175 0.1095 0.8170 0.8486 0.4672 0.4551 Rumpelstiltskin 0.0750 0.0858 0.8467 0.8569 0.4609 0.4478 The Goose Girl 0.1240 0.1178 0.8617 0.8624 0.4928 0.4643 The Story of Sigurd 0.1080 0.1178 0.8516 0.8670 0.4800 0.4664 Magic objects Aladdin and the Wonderful Lamp 0.0975 0.0910 0.8958 0.8664 0.4946 0.4559 Fortunatus and His Purse 0.1133 0.1185 0.8945 0.8306 0.5039 0.4519 The Golden Goose 0.1033 0.1155 0.9006 0.8529 0.5012 0.4611 The Magic Ring 0.1033 0.1040 0.9120 0.8960 0.5077 0.4762 Other stories Little Thumb 0.0300 0.1214 0.7444 0.8562 0.3872 0.4675 The Princess and the Pea 0.0300 0.0405 0.7444 0.7844 0.3872 0.3945 # Similarity within > across 10 (41.67%) 15 (62.50%) 19 (79.16%)
  • 36. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments Analysis & Discussion ? Errors in automatic construction (dependency parses aren’t really semantic graphs), e.g.: “along came a mouse” vs. “a mouse came”, coreference errors. ? Consistent with Fisseni & L?we (2012) findings: focus more on content & motifs? ? Combination of plot graph + BoW yields best results. 23/24
  • 37. Beijing 30 July ‘15 Folktales Plot graphs Similarity Experiments ? THANK YOU ? 24/24