Understanding narrative content has become an increasingly popular topic. Nonetheless, research on identifying common types of narrative characters, or personae, is impeded by the lack of automatic and broad-coverage evaluation methods. We argue that computationally modeling actors provides benefits, including novel evaluation mechanisms for personae. Specifically, we propose two actor-modeling tasks, cast prediction and versatility ranking, which can capture complementary aspects of the relation between actors and the characters they portray. For an actor model, we present a technique for embedding actors, movies, character roles, genres, and descriptive keywords as Gaussian distributions and translation vectors, where the Gaussian variance corresponds to actors' versatility. Empirical results indicate that (1) the technique considerably outperforms TransE ( 2013) and ablation baselines and (2) automatically identified persona topics (Bamman, O'Connor, and Smith 2013) yield statistically significant improvements in both tasks, whereas simplistic persona descriptors including age and gender perform inconsistently, validating prior research.
As a complex artifact, a narrative can be analyzed and computationally modeled as multiple layers that are dependent on each other, including the surface text @cite_16 , event semantics, and script-like patterns @cite_14 @cite_23 @cite_18 @cite_15 @cite_29 , macro-structures @cite_2 @cite_10 @cite_30 , story characters and their relations @cite_0 @cite_11 @cite_12 @cite_27 as well as narrative quality @cite_17 @cite_28 .
@cite_30 @cite_18 @cite_14 @cite_28 @cite_29 @cite_17 @cite_0 @cite_27 @cite_23 @cite_2 @cite_15 @cite_16 @cite_10 @cite_12 @cite_11
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Hand-coded scripts were used in the 1970-80s as knowledge backbones that enabled inference and other NLP tasks requiring deep semantic knowledge. We propose unsupervised induction of similar schemata called narrative event chains from raw newswire text. A narrative event chain is a partially ordered set of events related by a common protagonist. We describe a three step process to learning narrative event chains. The first uses unsupervised distributional methods to learn narrative relations between events sharing coreferring arguments. The second applies a temporal classifier to partially order the connected events. Finally, the third prunes and clusters self-contained chains from the space of events. We introduce two evaluations: the narrative cloze to evaluate event relatedness, and an order coherence task to evaluate narrative order. We show a 36 improvement over baseline for narrative prediction and 25 for temporal coherence. levels, such as Pet6fi's text grammar and Rumelhart's schemata for stories. By defining permissible interrelations between constituents of larger texts at all levels of abstraction, these create meaning structures organized on both hierarchical and associative principles, rather than on a sequential basis. The automated "understanding" process consists of instantiating an appropriate set of event templates by associating a given text with the ERGO inventory of event templates and const ucting a network of instanti ted templates based on intertemplate relational rules and constraints. The network of instantiated t mplates thus represents the information content of the unstructured original text, and provides the structured input for the event record data bas . Details of the automated understanding process presented here in the abstract are given in the follo ing section. 3.2 Processing Principles. The process of data base generation involves two major functions: (1) co tent analysis of the incoming text (2) event record synthesis (or production) The first involves constructing a meaning representation of the text and the second the extraction of relevant information and its storage in a data base record. The major focus of ERGO is on reports of a particular class of events which describe aircraft movements. The unit of analysis is therefore the report, a textual unit consisting of one or more par graphs, each containing one or more sentences. The first step in the analytical process involves a lexical lookup: a lexical entry contains morphological, syntactic and semantic information, on the lines of Sager (1973) and (1973). Each sentence is then subjected to a syntactic analysis by means of an Augmented Transition Network (ATN) parser (Woods, 1970). Since event templates are based on propositional structures, the analytical Narrative, and in particular storytelling, is an important part of the human experience. Consequently, computational systems that can reason about narrative can be more effective communicators, entertainers, educators, and trainers. One of the central challenges in computational narrative reasoning is narrative generation, the automated creation of meaningful event sequences. There are many factors - logical and aesthetic - that contribute to the success of a narrative artifact. Central to this success is its understandability. We argue that the following two attributes of narratives are universal: (a) the logical causal progression of plot, and (b) character believability. Character believability is the perception by the audience that the actions performed by characters do not negatively impact the audience's suspension of disbelief. Specifically, characters must be perceived by the audience to be intentional agents. In this article, we explore the use of refinement search as a technique for solving the narrative generation problem - to find a sound and believable sequence of character actions that transforms an initial world state into a world state in which goal propositions hold. We describe a novel refinement search planning algorithm - the Intent-based Partial Order Causal Link (IPOCL) planner - that, in addition to creating causally sound plot progression, reasons about character intentionality by identifying possible character goals that explain their actions and creating plan structures that explain why those characters commit to their goals. We present the results of an empirical evaluation that demonstrates that narrative plans generated by the IPOCL algorithm support audience comprehension of character intentions better than plans generated by conventional partial-order planners. This paper presents a digital storytelling approach that generates automatic animations for time-varying data visualization. Our approach simulates the composition and transition of storytelling techniques and synthesizes animations to describe various event features. Specifically, we analyze information related to a given event and abstract it as an event graph, which represents data features as nodes and event relationships as links. This graph embeds a tree-like hierarchical structure which encodes data features at different scales. Next, narrative structures are built by exploring starting nodes and suitable search strategies in this graph. Different stages of narrative structures are considered in our automatic rendering parameter decision process to generate animations as digital stories. We integrate this animation generation approach into an interactive exploration process of time-varying data, so that more comprehensive information can be provided in a timely fashion. We demonstrate with a storm surge application that our approach allows semantic visualization of time-varying data and easy animation generation for users without special knowledge about the underlying visualization techniques.