Character-based indexing and browsing with movie ontology1

Tran, Quang Dieu; Hwang, Dosam; Jung, Jason J.

doi:10.3233/jifs-169122

Cited by 8 publications

(7 citation statements)

References 18 publications

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“…In both cases, solving this problem requires using linguistic information: speech content (via transcripts, scripts, or subtitles), text overlaid in the video, predefined list of characters, other external resources. For instance, based on the assumption that the title of the fiction work is known, Tran et al [245] retrieve its list of characters from IMDb, look for their picture using Google Image, and leverage this information to infer character names in the movie through matching. In the context of fiction works though, the favored approach is to leverage scripts [118,148,190,282].…”

Section: /75mentioning

confidence: 99%

Extraction and Analysis of Fictional Character Networks

Labatut

Bost

2019

ACM Comput. Surv.

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A character network is a graph extracted from a narrative, in which vertices represent characters and edges correspond to interactions between them. A number of narrative-related problems can be addressed automatically through the analysis of character networks, such as summarization, classification, or role detection. Character networks are particularly relevant when considering works of fictions (e.g. novels, plays, movies, TV series), as their exploitation allows developing information retrieval and recommendation systems. However, works of fiction possess specific properties making these tasks harder.This survey aims at presenting and organizing the scientific literature related to the extraction of character networks from works of fiction, as well as their analysis. We first describe the extraction process in a generic way, and explain how its constituting steps are implemented in practice, depending on the medium of the narrative, the goal of the network analysis, and other factors. We then review the descriptive tools used to characterize character networks, with a focus on the way they are interpreted in this context. We illustrate the relevance of character networks by also providing a review of applications derived from their analysis. Finally, we identify the limitations of the existing approaches, and the most promising perspectives.

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Section: /75mentioning

confidence: 99%

Extraction and Analysis of Fictional Character Networks

Labatut

Bost

2019

ACM Comput. Surv.

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“…The proposed methods can be applied to resolving this issue because a story line is a set of scenes that have causal relationships. This point is similar in other applications: Story-based recommendation [6] or indexing [13,24]. We will present more details in Section 5 with further research directions.…”

Section: Assumption 2 (Independence Of Subplots)mentioning

confidence: 61%

“…Most of the existing studies analyzed stories based on social networks between characters (called as character networks) that appeared in narrative multimedia [2][3][4][5][6][7][8][9][10][11][12][13]. This S2 Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Plot Structure Decomposition in Narrative Multimedia by Analyzing Personalities of Fictional Characters

2021

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This study aims to decompose plot structures of stories in narrative multimedia (i.e., creative works that contain stories and are distributed through multimedia). Since a story is interwoven with main plots and subplots (i.e., primary and ancillary story lines), decomposing a story into multiple story lines enables us to analyze how events in the story are allocated and logically connected. For the decomposition, the existing studies employed character networks (i.e., social networks of characters that appeared in a story) and assumed that characters’ social relationships are consistent in a story line. However, these studies overlooked that social relationships significantly change around major events. To solve this problem, we attempt to use the changes for distinguishing story lines rather than suffer from the changes. We concentrate on the changes in characters’ social relationships being the result of changes in their personalities. Moreover, these changes gradually proceed within a story line. Therefore, we first propose features for measuring changes in personalities of characters: (i) Degrees of characters in character networks, (ii) lengths of dialogues spoken by characters, and (iii) ratios of out-degrees for in-degrees of characters in character networks. We supposed these features reflect importance, inner/outer conflicts, and activeness of characters, respectively. Since characters’ personalities gradually change in a story line, we can suppose that the features also show gradual story developments in a story line. Therefore, we conduct regression for each feature to discover dominant tendencies of the features. By filtering scenes that do not follow the tendencies, we extract a story line that exhibits the most dominant personality changes. We can decompose stories into multiple story lines by iterating the regression and filtering. Besides, personalities of characters change more significantly in major story lines. Based on this assumption, we also propose methods for discriminating main plots. Finally, we evaluated the accuracy of the proposed methods by applying them to the movies, which is one of the most popular narrative multimedia.

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“…Various studies [1][2][3][4] have been conducted for character networks (i.e., social networks between characters that appear in stories) to analyze stories in narrative multimedia (i.e., creative works that contain stories and are distributed through multimedia) automatically. They applied the analysis results on various applications-summarizing [5,6], recommending [7,8], indexing [9,10], and even generating [11] narrative multimedia. However, the character network model also has limitations.…”

Section: Introductionmentioning

confidence: 99%

Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia

Lee

Jung

Kim

2020

Sensors

Self Cite

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Narrative works (e.g., novels and movies) consist of various utterances (e.g., scenes and episodes) with multi-layered structures. However, the existing studies aimed to embed only stories in a narrative work. By covering other granularity levels, we can easily compare narrative utterances that are coarser (e.g., movie series) or finer (e.g., scenes) than a narrative work. We apply the multi-layered structures on learning hierarchical representations of the narrative utterances. To represent coarser utterances, we consider adjacency and appearance of finer utterances in the coarser ones. For the movies, we suppose a four-layered structure (character roles ∈ characters ∈ scenes ∈ movies) and propose three learning methods bridging the layers: Char2Vec, Scene2Vec, and Hierarchical Story2Vec. Char2Vec represents a character by using dynamic changes in the character’s roles. To find the character roles, we use substructures of character networks (i.e., dynamic social networks of characters). A scene describes an event. Interactions between characters in the scene are designed to describe the event. Scene2Vec learns representations of a scene from interactions between characters in the scene. A story is a series of events. Meanings of the story are affected by order of the events as well as their content. Hierarchical Story2Vec uses sequential order of scenes to represent stories. The proposed model has been evaluated by estimating the similarity between narrative utterances in real movies.

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Character-based indexing and browsing with movie ontology1

Cited by 8 publications

References 18 publications

Extraction and Analysis of Fictional Character Networks

Extraction and Analysis of Fictional Character Networks

Plot Structure Decomposition in Narrative Multimedia by Analyzing Personalities of Fictional Characters

Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia

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