Formal Models of Narrative Benedikt L owe Logic, Language and - - PowerPoint PPT Presentation

Formal Models of Narrative

Benedikt L¨

• we

Logic, Language and Computation. 19 September 2011

Set Theory. Infinite games. Set theory without the axiom of choice (in particular with the axiom of determinacy). Set theory of the reals. PhD student. Yurii Khomskii (until February 2012). PhD student. Zhenhao Li. Collaboration partners. Bonn (Germany), Denton TX (U.S.A.), New York NY (U.S.A.), Kobe (Japan), Ann Arbor MI (U.S.A.). Philosophy of Mathematics. Philosophy of mathematical practice. Empirical studies

• f mathematical research practice.

Collaboration partners. Utrecht (The Netherlands), Brussels (Belgium), Manchester (UK), K¨

• ln (Germany).

Applied logic. Formal representation of interaction, dialogues, historical logical systems and narratives.

• Postdoc. Dr. Carlos Le´
• n (Hamburg, starting 15 October

2011).

• Postdoc. Dr. Sara Uckelman (20%, until December 2011).
• Postdoc. Dr. Bernhard Fisseni (November 2011 to February

2012). PhD student. Sanchit Saraf (on leave in 2011/12). Collaboration partners. New York NY (U.S.A.), Tilburg (The Netherlands), Essen (Germany), Groningen (The Netherlands), T¨ ubingen (Germany), Lisbon (Portugal).

• Claim. There is a stable structural core of narratives; it is possible to objectively

extract the formal structure of a narrative.

• Claim. There is a stable structural core of narratives; it is possible to objectively

extract the formal structure of a narrative.

In fact, human beings are pretty good at detecting this structural core and discussing “identity” and “non-identity” of stories independent of the actual presentation of the story.

Example 1: Remake.

Example 1: Remake.

Example 2: The play vs the musical.

Example 2: The play vs the musical.

Example 3: The book vs the movie(s).

Example 3: The book vs the movie(s).

Theory of Analogy.

Theory of Analogy.

Dedre Gentner Ken Forbus Structure Mapping Theory. Structure Mapping Engine.

• B. Falkenhainer, K. Forbus, and D. Gentner: 1989, The structure-mapping en-

gine: Algorithm and examples. Artificial Intelligence, 20(41): 163.

Theory of Analogy.

Dedre Gentner Ken Forbus Structure Mapping Theory. Structure Mapping Engine.

• B. Falkenhainer, K. Forbus, and D. Gentner: 1989, The structure-mapping en-

gine: Algorithm and examples. Artificial Intelligence, 20(41): 163.

Formal systems together with their notion of isomorphism form a continuum of classifications of narratives into equivalence classes. The more expressive a system is, the smaller the equivalence classes are; i.e., fewer narratives are equivalent.

Karla the Hawk.

• M. J. Rattermann and D. Gentner.

Analogy and similarity: Determinants of accessibility and inferential soundness. In Proceedings of the Ninth Annual Con- ference of the Cognitive Science Society (1987), pp. 23-35:

Karla the Hawk.

• M. J. Rattermann and D. Gentner.

Analogy and similarity: Determinants of accessibility and inferential soundness. In Proceedings of the Ninth Annual Con- ference of the Cognitive Science Society (1987), pp. 23-35:

Karla, an old hawk, lived at the top of a tall oak tree. One afternoon, she saw a hunter on the ground with a bow and some crude arrows that had no feathers. The hunter took aim and shot at the hawk but missed. Karla knew the hunter wanted her feathers so she glided down to the hunter and offered to give him a few. The hunter was so grateful that he pledged never to shoot at a hawk again. He went off and shot deer instead.

Karla the Hawk.

• M. J. Rattermann and D. Gentner.

Analogy and similarity: Determinants of accessibility and inferential soundness. In Proceedings of the Ninth Annual Con- ference of the Cognitive Science Society (1987), pp. 23-35:

Karla, an old hawk, lived at the top of a tall oak tree. One afternoon, she saw a hunter on the ground with a bow and some crude arrows that had no feathers. The hunter took aim and shot at the hawk but missed. Karla knew the hunter wanted her feathers so she glided down to the hunter and offered to give him a few. The hunter was so grateful that he pledged never to shoot at a hawk again. He went off and shot deer instead. Once there was an eagle named Zerdia who donated a few of her tailfeathers to a sportsman and he promised never to attack eagles. One day Zerdia was nesting high on a rocky cliff when she saw the sportsman coming with a crossbow. Zerdia flew down to meet the man, but he attacked and felled her with a single bolt. As she fluttered to the ground Zerdia realized that the bolt had her

• wn tailfeathers on it.

Karla the Hawk.

• M. J. Rattermann and D. Gentner.

Analogy and similarity: Determinants of accessibility and inferential soundness. In Proceedings of the Ninth Annual Con- ference of the Cognitive Science Society (1987), pp. 23-35:

Karla, an old hawk, lived at the top of a tall oak tree. One afternoon, she saw a hunter on the ground with a bow and some crude arrows that had no feathers. The hunter took aim and shot at the hawk but missed. Karla knew the hunter wanted her feathers so she glided down to the hunter and offered to give him a few. The hunter was so grateful that he pledged never to shoot at a hawk again. He went off and shot deer instead. Once there was an eagle named Zerdia who donated a few of her tailfeathers to a sportsman and he promised never to attack eagles. One day Zerdia was nesting high on a rocky cliff when she saw the sportsman coming with a crossbow. Zerdia flew down to meet the man, but he attacked and felled her with a single bolt. As she fluttered to the ground Zerdia realized that the bolt had her

• wn tailfeathers on it.

Once there was a small country called Bildo that learned to make the worlds smartest computer. One day Bildo was attacked by its warlike neighbor, Gagrach. But the missiles were badly aimed and the attack failed. The Bildon government realized that Gagrach wanted Bildon computers so it offered to sell some of its computers to the country. The government of Gagrach was very

• pleased. It promised never to attack Bildo again.

Some criticism.

Some criticism.

• S. Lam, Affective analogical learning and reasoning, MSc Thesis, University of

Edinburgh, 2008. We have shown that [the] lack of inclusion of emotive content [in Gentner’s Structure Mapping Engine] has made it psychologically

• implausible. (p. 38)

Some criticism.

• S. Lam, Affective analogical learning and reasoning, MSc Thesis, University of

Edinburgh, 2008. We have shown that [the] lack of inclusion of emotive content [in Gentner’s Structure Mapping Engine] has made it psychologically

• implausible. (p. 38)
• I. Cornelisse, N. Venhuizen, The influence of emotion and sympathy on the

evaluation of story similarity, student project paper, Universiteit van Amsterdam, 2010. [A] story [with] different emotional content [and a] story ... imply[ing] a different feeling of sympathy ... are both [rated] significantly ... less similar to the Base Story than the True Analogy.

Formal systems.

Formal systems together with their notion of isomorphism form a continuum of classifications of narratives into equivalence classes. The more expressive a system is, the smaller the equivalence classes are; i.e., fewer narratives are equivalent.

Formal systems.

Formal systems together with their notion of isomorphism form a continuum of classifications of narratives into equivalence classes. The more expressive a system is, the smaller the equivalence classes are; i.e., fewer narratives are equivalent. The system we are looking for is

• 1. simple enough so that humans will not disagree about

whether a structure is the correct representation of the essence of a story,

Formal systems.

Formal systems together with their notion of isomorphism form a continuum of classifications of narratives into equivalence classes. The more expressive a system is, the smaller the equivalence classes are; i.e., fewer narratives are equivalent. The system we are looking for is

• 1. simple enough so that humans will not disagree about

whether a structure is the correct representation of the essence of a story,

• 2. expressive enough to capture all features relevant for the

notion of structural equivalence we’re aiming for.

Possible applications of an identified structural core.

Possible applications of an identified structural core.

• 1. Genre studies. Is there are correlation between the structure
• f the story and the genre? Do certain structures occur more

naturally in certain types of narratives?

Possible applications of an identified structural core.

• 1. Genre studies. Is there are correlation between the structure
• f the story and the genre? Do certain structures occur more

naturally in certain types of narratives?

• 2. Socio-cultural studies. Is there a correlation between the

structure of the story and the appeal of that story to a specific demographic group?

Possible applications of an identified structural core.

• 1. Genre studies. Is there are correlation between the structure
• f the story and the genre? Do certain structures occur more

naturally in certain types of narratives?

• 2. Socio-cultural studies. Is there a correlation between the

structure of the story and the appeal of that story to a specific demographic group?

• 3. Story generation. In computer games or other real-life

applications, pick the structure of a generated narratives based on the results of the other studies.

An analogy: Reasoning (1).

An analogy: Reasoning (1).

• Claim. There is a stable structural core of reasoning; it is possible to objectively

extract the formal structure of an argument.

An analogy: Reasoning (1).

• Claim. There is a stable structural core of reasoning; it is possible to objectively

extract the formal structure of an argument.

This structural core of reasoning is called logic. The first to “extract the formal structure” was Aristotle.

An analogy: Reasoning (1).

• Claim. There is a stable structural core of reasoning; it is possible to objectively

extract the formal structure of an argument.

This structural core of reasoning is called logic. The first to “extract the formal structure” was Aristotle.

All humans are mortal. Socrates is a human. Thus, Socrates is mortal.

An analogy: Reasoning (1).

• Claim. There is a stable structural core of reasoning; it is possible to objectively

extract the formal structure of an argument.

This structural core of reasoning is called logic. The first to “extract the formal structure” was Aristotle.

All humans are mortal. Socrates is a human. Thus, Socrates is mortal.

❀

All B are A. C is a B. Thus, B is A.

An analogy: Reasoning (2).

An analogy: Reasoning (2).

Stanovich, K. E., & West, R. F. (1998). Individual differences in rational thought. Journal of Experimental Psychology: General, 127, 161-188 Stanovich, K. E. (2003). The fundamental computational biases of human cog- nition: Heuristics that (sometimes) impair decision making and problem solving. In J. E. Davidson & R. J. Sternberg (Eds.), The psychology of problem solving (pp. 291–342). New York: Cambridge University Press.

An analogy: Reasoning (2).

Stanovich, K. E., & West, R. F. (1998). Individual differences in rational thought. Journal of Experimental Psychology: General, 127, 161-188 Stanovich, K. E. (2003). The fundamental computational biases of human cog- nition: Heuristics that (sometimes) impair decision making and problem solving. In J. E. Davidson & R. J. Sternberg (Eds.), The psychology of problem solving (pp. 291–342). New York: Cambridge University Press.

All living things need water. Roses need water. Thus, roses are living things.

An analogy: Reasoning (2).

Stanovich, K. E., & West, R. F. (1998). Individual differences in rational thought. Journal of Experimental Psychology: General, 127, 161-188 Stanovich, K. E. (2003). The fundamental computational biases of human cog- nition: Heuristics that (sometimes) impair decision making and problem solving. In J. E. Davidson & R. J. Sternberg (Eds.), The psychology of problem solving (pp. 291–342). New York: Cambridge University Press.

All living things need water. Roses need water. Thus, roses are living things.

About 70% of subjects agree that this is valid.

Formal systems for narratives (1).

• V. Propp, Morphology of the Folktale, Leningrad 1928

Formal systems for narratives (1).

• V. Propp, Morphology of the Folktale, Leningrad 1928

“Since [narratives are] exceptionally diverse, and evidently cannot be studied at once in [their] full extent, the material must be divided into sections, i.e., it must be classified. Correct classification is one of the first steps in a scientific description. The accuracy of all further study depends upon the accuracy of classification. (p. 5)”

Formal systems for narratives (1).

• V. Propp, Morphology of the Folktale, Leningrad 1928

“Since [narratives are] exceptionally diverse, and evidently cannot be studied at once in [their] full extent, the material must be divided into sections, i.e., it must be classified. Correct classification is one of the first steps in a scientific description. The accuracy of all further study depends upon the accuracy of classification. (p. 5)”

Propp’s formalization of Afanas’ev’s Tale 133: β1γ2ζ1η3δ2θ3A1 C ↑ [D1E 1neg]3[D1E 1neg]3Fcontr B4C ↑ [D1E 1pos]3[D1E 1pos]3

• H1-I 1K 4 ↓

Formal systems for narratives (2).

Formal systems for narratives (2).

Story Grammars.

• D. E. Rumelhart, Notes on a schema for stories, in: Representation and Under-

standing: Studies in cognitive science, 1975

Formal systems for narratives (2).

Story Grammars.

• D. E. Rumelhart, Notes on a schema for stories, in: Representation and Under-

standing: Studies in cognitive science, 1975

Plot Units.

• W. G. Lehnert, Plot Units and Narrative Summarization, Cognitive Science 4

(1981), pp. 293–331

Formal systems for narratives (3).

Formal systems for narratives (3).

TOPs (Thematic Organization Points).

• R. C. Schank, Dynamic memory: A theory of reminding and learning in computers

and people. 1982.

TAUs (Thematic Abstraction Units).

• M. G. Dyer, In-depth understanding: A computer model of integrated processing

for narrative comprehension. 1983.

PATs (Planning Advice Themes).

• S. Turner, The creative process. A computer model of storytelling. 1994.

Formal systems for narratives (3).

TOPs (Thematic Organization Points).

• R. C. Schank, Dynamic memory: A theory of reminding and learning in computers

and people. 1982.

TAUs (Thematic Abstraction Units).

• M. G. Dyer, In-depth understanding: A computer model of integrated processing

for narrative comprehension. 1983.

PATs (Planning Advice Themes).

• S. Turner, The creative process. A computer model of storytelling. 1994.
• B. L¨
• we, E. Pacuit, An abstract approch to reasoning about games with mistaken

and changing beliefs, Australasian Journal of Logic 6 (2008), pp. 162–181

• B. L¨
• we, E. Pacuit, S. Saraf, Identifying the structure of a narrative via an

agent-based logic of preferences and beliefs: Formalizations of episodes from CSI: Crime Scene InvestigationTM, MOCA’09

Comparison of formal systems.

Comparison of formal systems.

Let Σ be a formal system (with isomorphism relation ≃) and N, N∗ be narratives.

Comparison of formal systems.

Let Σ be a formal system (with isomorphism relation ≃) and N, N∗ be narratives. Suppose that Σ assigns unique structures Σ(N) and Σ(N∗) to the narratives.

Comparison of formal systems.

Let Σ be a formal system (with isomorphism relation ≃) and N, N∗ be narratives. Suppose that Σ assigns unique structures Σ(N) and Σ(N∗) to the narratives. Let N ≡Σ N∗ if and only if Σ(N) ≃ Σ(N∗).

Comparison of formal systems.

Let Σ be a formal system (with isomorphism relation ≃) and N, N∗ be narratives. Suppose that Σ assigns unique structures Σ(N) and Σ(N∗) to the narratives. Let N ≡Σ N∗ if and only if Σ(N) ≃ Σ(N∗). We compare two formal frameworks by studying the granularity of the relation ≡Σ. Fixing two different formal frameworks Σ and Σ∗ there are three cases:

Comparison of formal systems.

Let Σ be a formal system (with isomorphism relation ≃) and N, N∗ be narratives. Suppose that Σ assigns unique structures Σ(N) and Σ(N∗) to the narratives. Let N ≡Σ N∗ if and only if Σ(N) ≃ Σ(N∗). We compare two formal frameworks by studying the granularity of the relation ≡Σ. Fixing two different formal frameworks Σ and Σ∗ there are three cases: Case 1 Σ is a refinement of Σ∗. This means that for any two narratives N and N∗, if N ≡Σ∗ N∗, then N ≡Σ N∗.

Comparison of formal systems.

Let Σ be a formal system (with isomorphism relation ≃) and N, N∗ be narratives. Suppose that Σ assigns unique structures Σ(N) and Σ(N∗) to the narratives. Let N ≡Σ N∗ if and only if Σ(N) ≃ Σ(N∗). We compare two formal frameworks by studying the granularity of the relation ≡Σ. Fixing two different formal frameworks Σ and Σ∗ there are three cases: Case 1 Σ is a refinement of Σ∗. This means that for any two narratives N and N∗, if N ≡Σ∗ N∗, then N ≡Σ N∗. Case 2 Σ∗ is a refinement of Σ. This means that for any two narratives N and N∗, if N ≡Σ N∗, then N ≡Σ∗ N∗.

Comparison of formal systems.

Let Σ be a formal system (with isomorphism relation ≃) and N, N∗ be narratives. Suppose that Σ assigns unique structures Σ(N) and Σ(N∗) to the narratives. Let N ≡Σ N∗ if and only if Σ(N) ≃ Σ(N∗). We compare two formal frameworks by studying the granularity of the relation ≡Σ. Fixing two different formal frameworks Σ and Σ∗ there are three cases: Case 1 Σ is a refinement of Σ∗. This means that for any two narratives N and N∗, if N ≡Σ∗ N∗, then N ≡Σ N∗. Case 2 Σ∗ is a refinement of Σ. This means that for any two narratives N and N∗, if N ≡Σ N∗, then N ≡Σ∗ N∗. Case 3 The frameworks are incomparable. This means that there are narratives N0, N1, N2, and N4 such that N0 ≡Σ N1, N0 ≡Σ∗ N1. N2 ≡Σ∗ N3, and N2 ≡Σ N3.

Lehnert’s plot units

Lehnert’s plot units

• W. G. Lehnert, Plot Units and Narrative Summarization, Cognitive Science 4

(1981), pp. 293–331:

Lehnert’s plot units

• W. G. Lehnert, Plot Units and Narrative Summarization, Cognitive Science 4

(1981), pp. 293–331:

+ M

a

+

m

− M

a

M +

a

Doxastic preference framework

• B. L¨
• we, E. Pacuit, An abstract approch to reasoning about games with mistaken

and changing beliefs, Australasian Journal of Logic 6 (2008), pp. 162–181

• B. L¨
• we, E. Pacuit, S. Saraf, Identifying the structure of a narrative via an

agent-based logic of preferences and beliefs: Formalizations of episodes from CSI: Crime Scene InvestigationTM, MOCA’09

H v0 L v1 H v2 E v3 N v4 H v5 t0 t1 t2 t3 t4 t5 t6

S(v0, ∅)(H) = (t3, t0); S(v1, ∅)(L) = (t2, t1); S(v1, L)(H) = (t2, v3); S(v1, ∅)(H) = (t3, t2); S(v2, ∅)(H) = (t3, t2); S(v2, H)(E) = (t3, v4); S(v3, ∅)(E) = (v4, t3); S(v4, ∅)(N) = (t6, t4); S(v4, N)(H) = (t6, t5); S(v5, ∅)(H) = (t6, t5)

Comparison of PUF and DPF.

Comparison of PUF and DPF.

◮ DPF can easily express expectations, PUF can’t.

Comparison of PUF and DPF.

◮ DPF can easily express expectations, PUF can’t. ◮ PUF can identify individual actions as cause of other actions

which is difficult for DPF.

Comparison of PUF and DPF.

◮ DPF can easily express expectations, PUF can’t. ◮ PUF can identify individual actions as cause of other actions

which is difficult for DPF. We conclude that DPF and PUF are incomparable.

Comparison of PUF and DPF.

◮ DPF can easily express expectations, PUF can’t. ◮ PUF can identify individual actions as cause of other actions

which is difficult for DPF. We conclude that DPF and PUF are incomparable. The next step is to look at the separating stories and determine which of the frameworks gives the correct answer.

Comparison of PUF and DPF.

◮ DPF can easily express expectations, PUF can’t. ◮ PUF can identify individual actions as cause of other actions

which is difficult for DPF. We conclude that DPF and PUF are incomparable. The next step is to look at the separating stories and determine which of the frameworks gives the correct answer. Are expectations of the agents or causal relations relevant features of the structural type of a story?

Comparison of PUF and DPF.

◮ DPF can easily express expectations, PUF can’t. ◮ PUF can identify individual actions as cause of other actions

which is difficult for DPF. We conclude that DPF and PUF are incomparable. The next step is to look at the separating stories and determine which of the frameworks gives the correct answer. Are expectations of the agents or causal relations relevant features of the structural type of a story? If yes, add the feature to the system!

Comparison of PUF and DPF.

◮ DPF can easily express expectations, PUF can’t. ◮ PUF can identify individual actions as cause of other actions

which is difficult for DPF. We conclude that DPF and PUF are incomparable. The next step is to look at the separating stories and determine which of the frameworks gives the correct answer. Are expectations of the agents or causal relations relevant features of the structural type of a story? If yes, add the feature to the system! But this only works under the assumption that our formal system assigns formal structures to narratives objectively.

Analogy 2: Language (1).

Analogy 2: Language (1).

• Claim. There is a stable structural core of grammar; it is possible to objectively

extract the formal structure of an utterance.

Analogy 2: Language (1).

• Claim. There is a stable structural core of grammar; it is possible to objectively

extract the formal structure of an utterance.

The dog ate the bone.

Analogy 2: Language (1).

• Claim. There is a stable structural core of grammar; it is possible to objectively

extract the formal structure of an utterance.

The dog ate the bone. ❀

Analogy 2: Language (1).

• Claim. There is a stable structural core of grammar; it is possible to objectively

extract the formal structure of an utterance.

The dog ate the bone. ❀ Untrained people will not be able to do the formalization, but competent trained people will agree that that the formalization is correct.

Analogy 2: Language (2).

Analogy 2: Language (2).

Sentence — Discourse — Narrative

Analogy 2: Language (2).

Sentence — Discourse — Narrative Armchair linguistics versus Corpus linguistics.

Analogy 2: Language (2).

Sentence — Discourse — Narrative Armchair linguistics versus Corpus linguistics.

• R. Artstein, M. Poesio.

Inter-coder agreement for computational linguistics. Computational Linguistics 34(4): 555–596, 2008:

Analogy 2: Language (2).

Sentence — Discourse — Narrative Armchair linguistics versus Corpus linguistics.

• R. Artstein, M. Poesio.

Inter-coder agreement for computational linguistics. Computational Linguistics 34(4): 555–596, 2008: Ever since the mid-[1990s], increasing effort has gone into putting semantics and discourse research on the same empirical footing as

• ther areas of Computational Linguistics. This soon led to worries

about the subjectivity of the judgments required to create annotated resources, much greater for semantics and pragmatics than for [other areas of linguistics].

Analogy 2: Language (2).

Sentence — Discourse — Narrative Armchair linguistics versus Corpus linguistics.

• R. Artstein, M. Poesio.

Inter-coder agreement for computational linguistics. Computational Linguistics 34(4): 555–596, 2008: Ever since the mid-[1990s], increasing effort has gone into putting semantics and discourse research on the same empirical footing as

• ther areas of Computational Linguistics. This soon led to worries

about the subjectivity of the judgments required to create annotated resources, much greater for semantics and pragmatics than for [other areas of linguistics].

At the level of narrative, we are still very much in the armchair phase.

Inter-annotator agreement.

Inter-annotator agreement.

• J. C. Carletta, A. Isard, S. Isard, J. Kowtko, G. Doherty-Sneddon, and A. An-
• derson. The reliability of a dialogue structure coding scheme. Computational

Linguistics, 23(1):13–31, 1997.

• D. Marcu, M. Romera, and E. A. Amorrortu. Experiments in constructing a

corpus of discourse trees: Problems, annotation choices, issues. In: Workshop

• n Levels of Representation in Discourse, pages 71–78, 1999.

Inter-annotator agreement.

• J. C. Carletta, A. Isard, S. Isard, J. Kowtko, G. Doherty-Sneddon, and A. An-
• derson. The reliability of a dialogue structure coding scheme. Computational

Linguistics, 23(1):13–31, 1997.

• D. Marcu, M. Romera, and E. A. Amorrortu. Experiments in constructing a

corpus of discourse trees: Problems, annotation choices, issues. In: Workshop

• n Levels of Representation in Discourse, pages 71–78, 1999.

Questions.

Inter-annotator agreement.

• J. C. Carletta, A. Isard, S. Isard, J. Kowtko, G. Doherty-Sneddon, and A. An-
• derson. The reliability of a dialogue structure coding scheme. Computational

Linguistics, 23(1):13–31, 1997.

• D. Marcu, M. Romera, and E. A. Amorrortu. Experiments in constructing a

corpus of discourse trees: Problems, annotation choices, issues. In: Workshop

• n Levels of Representation in Discourse, pages 71–78, 1999.

Questions.

◮ Do narrative annotations give rise to objective results in the

same sense that annotations at the sentence level do? (This has not even done with Propp yet!)

Inter-annotator agreement.

• J. C. Carletta, A. Isard, S. Isard, J. Kowtko, G. Doherty-Sneddon, and A. An-
• derson. The reliability of a dialogue structure coding scheme. Computational

Linguistics, 23(1):13–31, 1997.

• D. Marcu, M. Romera, and E. A. Amorrortu. Experiments in constructing a

corpus of discourse trees: Problems, annotation choices, issues. In: Workshop

• n Levels of Representation in Discourse, pages 71–78, 1999.

Questions.

◮ Do narrative annotations give rise to objective results in the

same sense that annotations at the sentence level do? (This has not even done with Propp yet!)

◮ If yes, how close is this to the original intuitive notion of

structural equivalence of narratives?