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LINGUIST List 24.2936

Thu Jul 18 2013

Review: Computational Linguistics; Linguistics & Literature: Mani (2012)

Editor for this issue: Joseph Salmons <jsalmonslinguistlist.org>

Date: 02-Jun-2013
From: Choonkyu Lee <c.leeuu.nl>
Subject: Computational Modeling of Narrative
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Book announced at http://linguistlist.org/issues/24/24-485.html

AUTHOR: Inderjeet Mani
TITLE: Computational Modeling of Narrative
SERIES TITLE: Synthesis Lectures on Human Language Technologies
PUBLISHER: Morgan & Claypool Publishers
YEAR: 2012

REVIEWER: Choonkyu Lee, Utrecht Institute of Linguistics OTS

This book presents core concepts and insights from humanities narratology,
such as time, plot, and the narrator, for automatic narrative understanding
and generation in natural language processing and artificial intelligence,
including interactive entertainment. It also describes how computational
developments may also contribute to narratological theory and digital
humanities. The book is mainly a primer on narratology and a review of
computational developments for computational linguists and game developers,
but may also be of interest to cognitive scientists and narratologists
interested in applications.

Chapter 1, Narratological Background, provides an overview of the main
narratological concepts addressed and establishes terminology. Mani defines
‘narrative’ with a pre-theoretic notion of ‘story,’ to include parts of blogs,
emails, news, and technical and literary works, among others, that involve
storytelling with causal coherence. The relevant modes of presentation (or
media) also include games, as game developers with an interest in interactive
narrative are among the primary intended audience for the book. The author
also defines ‘Narrative Structure’ to encompass global dimensions of phenomena
related to narrative representation, including narrator and audience
variables. In this way, his view of narrative structure goes beyond discourse
formalisms such as Discourse Representation Theory (Kamp, 1984) and Rhetorical
Structure Theory (Mann & Thompson, 1988), which focus on relations of
coreference, time, and/or communicative roles at a more local level. One of
the most important distinctions that Mani makes -- relevant throughout the
book -- is between ‘fabula’ as the underlying (story) content and ‘discourse’
as its expression or form. In a section on narrator characteristics, he
introduces Genette’s (1980) narratological concepts of a homodiegetic narrator
(who participates in the story) and a heterodiegetic narrator (who doesn’t),
as well as the narrative distance continuum (Genette, 1988), which is
represented in Mani’s annotation scheme, NarrativeML, with six values, ranging
from narrated speech to immediate speech. Mani also introduces different cases
of narrator perspective: non-focalized (omniscient), internally-focalized, and
externally-focalized (Genette, 1980), with room for ‘other’ possibilities in
NarrativeML. He also discusses embedded stories, as in “The Thousand and One
Nights,” in which readers often have to revise their beliefs about the factual
status of characters or events. Subordinated discourse with propositional
attitudes also helps determine the factual status of events. With regard to
narrative time, Mani introduces Genette’s (1980) narrative time relations --
subsequent, simultaneous, and prior -- indicated by the dominant tense in the
narrative. He also mentions Chatman’s (1980) distinction between story time
(in the fabula) and discourse time -- discourse processing time in Chatman’s
original formulation, but operationalized as textual length for Mani’s
NarrativeML. Mani uses the ratio between the two to represent narrative pace,
which indicates pacing techniques such as stretching and speed-up. Seven types
of narrative ordering from Genette’s (1980) account -- chronological
(‘Chronicle’) and others -- are also represented in NarrativeML, indicating
the relationship between discourse order and the actual order of events in the
fabula. The issue of audience response, or reader affect, is also mentioned.
After a brief discussion of the ontology for a fabula or story world, Mani
also introduces accessibility relations, used for representing narrative
embeddings and subordinated discourse with propositional attitudes while
avoiding the representation of multiple worlds (multiple story worlds and the
actual world). Changes in accessibility relations can represent the audience’s
belief revision, e.g., in a plot twist. The chapter ends with a description of
NarrativeML, an annotation scheme that incorporates many of the narratological
constructs. Such a scheme can be used for human annotation of corpora, and the
annotated corpora can then be used for supervised learning of systems for
automatic narrative understanding and generation. The possibility of importing
other annotations from the PropBank (Palmer, Gildea & Kingsbury, 2005) and
other schemes is also discussed.

In Chapter 2, Characters as Intentional Agents, Mani presents the ‘planning
perspective’ on narrative, in which the task of narrative understanding is to
recognize the characters’ goals and plans, and that of narrative generation is
to synthesize a series of plans. Since there have been more developments in AI
on intentionality than in humanities narratology, Mani focuses more on the AI
side in this chapter while suggesting how these developments may provide
insight for narratology. Interpreting actions and events in terms of goals and
plans presumes world knowledge. Preconditions and consequences for actions and
stereotypical structures of event sequences have been represented in narrative
understanding systems, such as those based on Schank and Abelson’s (1977)
scripts, but Mani points out that these systems were too domain-specific and
focused too much on events rather than characters. He also mentions the use of
case-based reasoning in both understanding and generation, which allows
generalization from stored narrative fragments beyond simple retrieval. In
particular, he points out some general limitations in story planning systems,
such as domain specificity, and lack of natural variation in narrative order,
distance, etc. For purposes of ‘lightweight’ annotation with NarrativeML, Mani
adopts Pavel’s (1982) Move-grammar, which provides a coarse-grained analysis
of goal structure in the fabula -- often corresponding to long stretches of
text. Pavel’s analysis decomposes an action into a problem and a solution. In
the section on interactive narrative, which often involves incremental,
non-monotonic plan revision after new events, Mani discusses Mateas and
Stern’s (2005) FAÇADE to illustrate dynamic re-planning in reaction to
audience feedback, but points out the problem of a large number of branching
possibilities having to be spelled out. He suggests that a good user model for
reader affect or aesthetic preference may help constrain possible paths in
interactive narrative generation, proposing a Boolean model of the reader’s
attitude toward the agent of an event -- positive (sympathy), negative
(antipathy), or neutral. Mani also mentions the possibility of using sentiment
analysis for a model of narrator attitudes as well, though not represented in
his NarrativeML. He also discusses the problem of balance between authorial
and audience control in interactive narrative. The representations of
intentionality discussed in this chapter are closely tied to Chapter 4, Plot.

In Chapter 3, Time, Mani focuses on narrative understanding for story time.
After a quick review of rule-based systems making use of tense and aspectual
marking or causal knowledge, he introduces Allen’s (1984) interval calculus
with seven basic temporal relations, which is adopted in NarrativeML and can
be extended with logics for branching time for underspecified relations. Mani
then discusses TIMEX2 (Ferro et al., 2005) and TimeML (Pustejovsky et al.,
2005) annotation schemes for tagging duration, end time, and relations among
events and times, in which subscript indices indicate the order of mention,
allowing inferences about narrative ordering in relation to the actual order
of events in the story world. Subordinating links, or SLINKs, for relations
such as remembering require branching time models. Discourse time, measured in
number of words, can be compared to story time in the fabula to measure
narrative pace, or tempo. The author also mentions an interesting possible
extension with estimates of minimum and maximum duration of events to
represent commonsense intuitions (Pan, Mulkar-Mehta & Hobbs, 2011). Issues in
human annotation with temporal links among times and events, or TLINKs, are
discussed. Successful automatic tagging in these temporal aspects of narrative
can facilitate narratological investigations. In a brief section on automatic
narrative generation, Mani describes two particular systems, Callaway’s (2000)
STORYBOOK and Montfort’s (2011) CURVESHIP. Back to narrative understanding,
Mani reviews recent success in automatic tagging and resolution of temporal
expressions, as well as recent developments in automatic tagging of events,
factuality, coarse-grained duration, and TLINKs. For temporal relation
classifier systems based on local pair-wise decisions, the problem of global
inconsistency may arise. Solutions combining a ranking method with Integer
Linear Programming or Markov Logic Networks are discussed. Habituals and
scene-setting descriptions also pose a challenge to narrative time

In Chapter 4, Plot, Mani provides background on important concepts, including
abstract event summaries and Aristotelian mythos, based on a view of plot as a
compact structural unit with emphasis on event sequences. Other narratological
concepts regarding plot, such as a turning point, a narrative arc with stages,
the heroic quest, and Propp’s (1968) narrative functions, are discussed, along
with applications to interactive narrative. Overly fine-grained distinctions
are often impractical for reliable annotation, but the use of more global
structural representations based on story grammars (Rumelhart, 1977) and
Macrostructures (van Dijk, 1980) in automatic story generation is discussed
next. In this section, Mani repeats the distinction between story time at the
level of fabula and discourse time as text length, pointing out that
generation systems often fail to decouple the two and claiming that Pavel’s
Move-grammar adopted into Mani’s NarrativeML may improve upon that aspect.
Mental states and intentionality in plot are then emphasized, with
descriptions of previous accounts involving affect states (+, -, and a neutral
M; Lehnert, 1981), which capture motivations and intentional actualizations.
Despite the coarse-grained differentiation of emotional states, classifying
the (emotional) polarity of verbs based on their arguments may improve
representation of causality (Goyal, Riloff & Daumé III, 2010). Recurring
patterns of transitions are considered plot units, and applications including
Elson’s (2012) Story Intention Graphs for DramaBank are discussed.
Acknowledging the inherent difficulty of inferring intentional states that are
not directly expressed, Mani mentions event summarization approaches, such as
Chambers’ (2011) Narrative Event Chain, combined with salience filtering or
event-based causal reasoning algorithms as a potentially more feasible
alternative. After a quick overview comparing the different plot models
discussed in the chapter, Mani suggests some applications for narratology,
such as intelligent searching for stories with similar plots.

In the final chapter, Summary and Future Directions, the author summarizes
each previous chapter, and illustrates the representation of the major aspects
of a narrative in NarrativeML with an example (pp. 96-99), mentioning the
problem that long literary genres with substructural units such as scenes or
episodes would pose a challenge to annotation efforts. In his concluding
remarks, Mani speculates that developments in narratological theories of
character psychology may continue the tradition of narratological insights for
computational applications, and also hints at the opposite direction of
inspiration, namely, automatic narrative computing systems inspiring
developments in digital humanities with enhanced search, analysis,
translation, clustering, and recommendation, among others.

This book offers an easy-to-read introduction to the core issues in narrative
representation, both traditional narratological insights and more recent
computational developments. Mani defines the terminology and explains his
choice of terms carefully, and suggests promising interdisciplinary
contributions between humanities narratology and computational narratology
throughout, achieving his main goals. He concludes each chapter with a clear
illustration of how the aspects of narrative discussed in the chapter are
represented in NarrativeML.

The book is primarily intended for computer scientists working on narrative
processing and generation and for narrative theorists interested in
applications, and its direct relevance to other fields of cognitive science,
including formal semantics, seems more limited than the back cover suggests.
For example, Mani himself points out that he is more concerned with an entire
story at a more global level, compared to the more local focus of Discourse
Representation Theory or Rhetorical Structure Theory. In addition, although
some interesting experimental findings (e.g., Gerrig & Bernardo, 1994) and
human annotation studies (e.g., Pan, Mulkar-Mehta & Hobbs, 2011) are
mentioned, discussion of relevant findings in cognitive psychology (e.g.,
Graesser, Singer & Trabasso, 1994, on causality and intentionality; Zwaan &
Radvansky, 1998, on the situation model of discourse) is otherwise lacking.
Comparing NarrativeML representation to Zwaan and Radvansky’s (1998) situation
model reveals that the core dimensions of narrative align well between the two
models, but that in NarrativeML spatial relations are not represented to a
level of granularity that the situation model would predict or to the same
level of granularity as temporal relations in NarrativeML. Incorporating
Mani’s recent work (Mani & Pustejovsky, 2012) on spatial representation may be
useful for narrative representation as well. Another possible addition to
NarrativeML is to represent character prominence, which has been found to be
important for narrative production (e.g., Sanford, Moar & Garrod, 1988).

Narrative computing is an exciting field, very much burgeoning, and it will be
interesting to see how Mani’s proposals, e.g., the use of Pavel’s (1982)
Move-grammar for representing character goals, stand the test of time.

Allen, J. (1984). Towards a general theory of action and time. Artificial
Intelligence, 23(2), 123-154.

Callaway, C. (2000). Narrative prose generation. Doctoral dissertation, North
Carolina State University.

Chatman, S. (1980). Story and discourse: Narrative structure in fiction and
film. Ithaca: Cornell University Press.

Ferro, L., Gerber, L., Mani, I., Sundheim, B. & Wilson, G. (2005). TIDES 2005
standard for the annotation of temporal expressions.

Genette, G. (1980). Narrative discourse (J. Lewin, Trans.). Ithaca: Cornell
University Press.

Genette, G. (1988). Narrative discourse revisited (J. Lewin, Trans.). Ithaca:
Cornell University Press.

Gerrig, R. & Bernardo, D. (1994). Readers as problem-solvers in the experience
of suspense. Poetics, 22, 459-472.

Goyal, A., Riloff, E. & Daumé III, H. (2010). Automatically producing plot
unit representations for narrative text. In Proceedings of EMNLP 2010 (pp.
77-86). Cambridge, MA.

Graesser, A. C., Singer, M. & Trabasso, T. (1994) Constructing inferences
during narrative text comprehension. Psychological Review, 101(3), 371-395.

Kamp, H. (1984). A theory of truth and semantic representation. In J.A.G.
Groenendijk, T.M.V. Janssen & M.B.J. Stockhof (Eds.), Truth, Interpretation,
and Information (pp. 277-322). Dordrecht: Foris.

Lehnert, W. G. (1981). Plot Units: A narrative summarization strategy. In W.G.
Lehnert & M.H. Ringle (Eds.), Strategies for Natural Language Processing.
Hillsdale, NJ: Lawrence Erlbaum.

Mani, I. & Pustejovsky, J. (2012). Interpreting motion: Grounded
representations for spatial language. New York: Oxford University Press.

Mann, W.C. & Thompson, S.A. (1988). Rhetorical structure theory: Towards a
functional theory of text organization. Text, 8(3), 243-281.

Mateas, M. & Stern, A. (2005). Structuring content in the Façade interactive
drama architecture. In Proceedings of the AIIDE 2005 (pp. 93-98). Menlo Park,
CA: AAAI Press.

Montfort, N. (2011). Curveship’s automatic narrative variation. In Proceedings
of the 6th International Conference on the Foundations of Digital Games (pp.

Palmer, M., Gildea, D. & Kingsbury, P. (2005). The Proposition Bank: A corpus
annotated with semantic roles. Computational Linguistics, 31(1), 71-105.

Pan, F., Mulkar-Mehta, R. & Hobbs, J.R. (2011). Annotating and learning event
durations in text. Computational Linguistics, 37(4), 727-752.

Pavel, T. (1982). Plot-structure and style: Remarks on an unstable
relationship. Canadian Review of Comparative Literature, 9(1), 27-45.

Propp, V. (1968). Morphology of the folktale (L. Scott, Trans.). Austin:
University of Texas Press.

Pustejovsky, J., Ingria, B., Saurí, R., Castano, J., Littman, J. Gaizauskas,
R., Setzer, A., Katz, G. & Mani, I. (2005). The specification language TimeML.
In I. Mani & J. Pustejovsky (Eds)., The language of time: A reader (pp.
545-558). New York: Oxford University Press.

Sanford, A.J., Moar, K. & Garrod, S.C. (1988). Proper names as controllers of
discourse focus. Language and Speech, 31(1), 43-56.

Schank, R.C. & Abelson, R.P. (1977). Scripts, plans, goals, and understanding:
An inquiry into human knowledge structures. Hillsdale, NJ: Lawrence Erlbaum.

Zwaan, R.A. & Radvansky, G.A. (1998). Situation models in language
comprehension and memory. Psychological Bulletin, 123(2), 162-185.

Choonkyu Lee is a postdoctoral researcher at the Utrecht Institute of
Linguistics OTS. His research interests include time in narrative discourse
and commonsense knowledge in semantics/pragmatics, with interdisciplinary
approaches involving cognitive psychology, theoretical linguistics, and
computational linguistics.
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