Pub Date : 2016-09-30DOI: 10.4230/OASIcs.CMN.2016.6
Joshua D. Eisenberg, W. V. Yarlott, Mark A. Finlayson
Having access to a large set of stories is a necessary first step for robust and wide-ranging computational narrative modeling; happily, language data - including stories - are increasingly available in electronic form. Unhappily, the process of automatically separating stories from other forms of written discourse is not straightforward, and has resulted in a data collection bottleneck. Therefore researchers have sought to develop reliable, robust automatic algorithms for identifying story text mixed with other non-story text. In this paper we report on the reimplementation and experimental comparison of the two approaches to this task: Gordon's unigram classifier, and Corman's semantic triplet classifier. We cross-analyze their performance on both Gordon's and Corman's corpora, and discuss similarities, differences, and gaps in the performance of these classifiers, and point the way forward to improving their approaches.
{"title":"Comparing Extant Story Classifiers: Results & New Directions","authors":"Joshua D. Eisenberg, W. V. Yarlott, Mark A. Finlayson","doi":"10.4230/OASIcs.CMN.2016.6","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2016.6","url":null,"abstract":"Having access to a large set of stories is a necessary first step for robust and wide-ranging computational narrative modeling; happily, language data - including stories - are increasingly available in electronic form. Unhappily, the process of automatically separating stories from other forms of written discourse is not straightforward, and has resulted in a data collection bottleneck. Therefore researchers have sought to develop reliable, robust automatic algorithms for identifying story text mixed with other non-story text. In this paper we report on the reimplementation and experimental comparison of the two approaches to this task: Gordon's unigram classifier, and Corman's semantic triplet classifier. We cross-analyze their performance on both Gordon's and Corman's corpora, and discuss similarities, differences, and gaps in the performance of these classifiers, and point the way forward to improving their approaches.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126385518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-09-30DOI: 10.4230/OASIcs.CMN.2016.14
O. D. Ninan, George O. Ajíbádé, O. A. Odejobi
Our effort at developing computational models for African narratives, particularly those of Yoruba folktales, is challenged by the diversity in concepts and methodologies in the discipline. This motivated us to pause and consider the various computational models of narratives in the literature. This is with a view to finding the most appropriate or otherwise adapt a closely related one for the purpose. Thorndyke's story grammar was among the models of narrative in the literature which were appraised, found close in structure and was adapted for the modelling of Yoruba folktales narrative. In conclusion we found that the modified version of Thorndyke's model was appropriate for modelling Yoruba folktales narrative.
{"title":"Appraisal of Computational Model for Yorùbá Folktale Narrative","authors":"O. D. Ninan, George O. Ajíbádé, O. A. Odejobi","doi":"10.4230/OASIcs.CMN.2016.14","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2016.14","url":null,"abstract":"Our effort at developing computational models for African narratives, particularly those of Yoruba folktales, is challenged by the diversity in concepts and methodologies in the discipline. This motivated us to pause and consider the various computational models of narratives in the literature. This is with a view to finding the most appropriate or otherwise adapt a closely related one for the purpose. Thorndyke's story grammar was among the models of narrative in the literature which were appraised, found close in structure and was adapted for the modelling of Yoruba folktales narrative. In conclusion we found that the modified version of Thorndyke's model was appropriate for modelling Yoruba folktales narrative.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123205806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-06DOI: 10.4230/OASIcs.CMN.2015.98
Folgert Karsdorp, M. Kestemont, Christof Schöch, Antal van den Bosch
We report on building a computational model of romantic relationships in a corpus of historical literary texts. We frame this task as a ranking problem in which, for a given character, we try to assign the highest rank to the character with whom (s)he is most likely to be romantically involved. As data we use a publicly available corpus of French 17th and 18th century plays (http://www.theatre-classique.fr/) which is well suited for this type of analysis because of the rich markup it provides (e.g. indications of characters speaking). We focus on distributional, so-called second-order features, which capture how speakers are contextually embedded in the texts. At a mean reciprocal rate (MRR) of 0.9 and MRR@1 of 0.81, our results are encouraging, suggesting that this approach might be successfully extended to other forms of social interactions in literature, such as antagonism or social power relations.
{"title":"The Love Equation: Computational Modeling of Romantic Relationships in French Classical Drama","authors":"Folgert Karsdorp, M. Kestemont, Christof Schöch, Antal van den Bosch","doi":"10.4230/OASIcs.CMN.2015.98","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2015.98","url":null,"abstract":"We report on building a computational model of romantic relationships in a corpus of historical literary texts. We frame this task as a ranking problem in which, for a given character, we try to assign the highest rank to the character with whom (s)he is most likely to be romantically involved. As data we use a publicly available corpus of French 17th and 18th century plays (http://www.theatre-classique.fr/) which is well suited for this type of analysis because of the rich markup it provides (e.g. indications of characters speaking). We focus on distributional, so-called second-order features, which capture how speakers are contextually embedded in the texts. At a mean reciprocal rate (MRR) of 0.9 and MRR@1 of 0.81, our results are encouraging, suggesting that this approach might be successfully extended to other forms of social interactions in literature, such as antagonism or social power relations.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133613398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-05-26DOI: 10.4230/OASIcs.CMN.2015.142
Matthew J. Thompson, J. Padget, Steve Battle
A narrative world can be viewed as a form of society in which characters follow a set of social norms whose collective function is to guide the characters through (the creation of) a story arc and reach some conclusion. By modelling the rules of a narrative using norms, we can govern the actions of agents that act out the characters in a story. Agents are given sets of permitted actions and obligations to fulfil based on their and the story's current situation. However, the decision to conform to these expectations is ultimately left to the agent. This means that the characters have control over fine-grained elements of the story, resulting in a more flexible and dynamic narrative experience. This would allow the creator of an interactive narrative to specify only the general structure of a story, leaving the details to the agents. We illustrate a particular realisation of this vision using a formalization of Propp's morphology in a normative social framework, with belief-desire-intention agents playing the characters.
{"title":"Governing Narrative Events With Institutional Norms","authors":"Matthew J. Thompson, J. Padget, Steve Battle","doi":"10.4230/OASIcs.CMN.2015.142","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2015.142","url":null,"abstract":"A narrative world can be viewed as a form of society in which characters follow a set of social norms whose collective function is to guide the characters through (the creation of) a story arc and reach some conclusion. By modelling the rules of a narrative using norms, we can govern the actions of agents that act out the characters in a story. Agents are given sets of permitted actions and obligations to fulfil based on their and the story's current situation. However, the decision to conform to these expectations is ultimately left to the agent. This means that the characters have control over fine-grained elements of the story, resulting in a more flexible and dynamic narrative experience. This would allow the creator of an interactive narrative to specify only the general structure of a story, leaving the details to the agents. We illustrate a particular realisation of this vision using a formalization of Propp's morphology in a normative social framework, with belief-desire-intention agents playing the characters.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132662079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-05-01DOI: 10.4230/OASIcs.CMN.2015.82
Folgert Karsdorp, M. V. D. Meulen, T. Meder, Antal van den Bosch
This paper presents a linguistically uninformed computational model for animacy classification. The model makes use of word n-grams in combination with lower dimensional word embedding representations that are learned from a web-scale corpus. We compare the model to a number of linguistically informed models that use features such as dependency tags and show competitive results. We apply our animacy classifier to a large collection of Dutch folktales to obtain a list of all characters in the stories. We then draw a semantic map of all automatically extracted characters which provides a unique entrance point to the collection.
{"title":"Animacy Detection in Stories","authors":"Folgert Karsdorp, M. V. D. Meulen, T. Meder, Antal van den Bosch","doi":"10.4230/OASIcs.CMN.2015.82","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2015.82","url":null,"abstract":"This paper presents a linguistically uninformed computational model for animacy classification. The model makes use of word n-grams in combination with lower dimensional word embedding representations that are learned from a web-scale corpus. We compare the model to a number of linguistically informed models that use features such as dependency tags and show competitive results. We apply our animacy classifier to a large collection of Dutch folktales to obtain a list of all characters in the stories. We then draw a semantic map of all automatically extracted characters which provides a unique entrance point to the collection.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"358 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122739312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-05-01DOI: 10.4230/OASIcs.CMN.2015.157
P. Winston
A story summarizer benefits greatly from a reader model because a reader model enables the story summarizer to focus on delivering useful knowledge in minimal time with minimal eort. Such a
{"title":"Model-based Story Summary","authors":"P. Winston","doi":"10.4230/OASIcs.CMN.2015.157","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2015.157","url":null,"abstract":"A story summarizer benefits greatly from a reader model because a reader model enables the story summarizer to focus on delivering useful knowledge in minimal time with minimal eort. Such a","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127797172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-08-02DOI: 10.4230/OASIcs.CMN.2014.121
H. Murai
This paper proposes a method of describing narrative structure, that focuses on the behavior of the characters in the story. It also proposes to assign the concepts of focus, polarity, dynamic, motivation, and result as attributes of behavior. Utilizing these attributes, the plots of shortshort stories by Shinichi Hoshi can be represented formally. Moreover, the method presented here shows that some reversal punch-line patterns can be described using the data captured from plot representations. 1998 ACM Subject Classification I.7.5 Document analysis
{"title":"Plot Analysis for Describing Punch Line Functions in Shinichi Hoshi's Microfiction","authors":"H. Murai","doi":"10.4230/OASIcs.CMN.2014.121","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2014.121","url":null,"abstract":"This paper proposes a method of describing narrative structure, that focuses on the behavior of the characters in the story. It also proposes to assign the concepts of focus, polarity, dynamic, motivation, and result as attributes of behavior. Utilizing these attributes, the plots of shortshort stories by Shinichi Hoshi can be represented formally. Moreover, the method presented here shows that some reversal punch-line patterns can be described using the data captured from plot representations. 1998 ACM Subject Classification I.7.5 Document analysis","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124445865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-07-31DOI: 10.4230/OASIcs.CMN.2014.13
B. B. Miranda, S. Caffiau, C. Garbay, François Portet
Automatic story generation is the subject of a growing research effort. However, in this domain, stories are generally produced from fictional data. In this paper, we present a task model used for automatic story generation from real data focusing on the narrative planning. The aim is to generate recits (stories) from sensors data acquired during a ski sortie. The model and some preliminary analysis are presented which suggest the interest of the approach.
{"title":"A Task Based model for Récit Generation from Sensor Data: An Early Experiment","authors":"B. B. Miranda, S. Caffiau, C. Garbay, François Portet","doi":"10.4230/OASIcs.CMN.2014.13","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2014.13","url":null,"abstract":"Automatic story generation is the subject of a growing research effort. However, in this domain, stories are generally produced from fictional data. In this paper, we present a task model used for automatic story generation from real data focusing on the narrative planning. The aim is to generate recits (stories) from sensors data acquired during a ski sortie. The model and some preliminary analysis are presented which suggest the interest of the approach.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128331739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-07-31DOI: 10.4230/OASIcs.CMN.2014.177
Antoine Saillenfest, J. Dessalles
In this work, we address the question of generating understandable narratives using a cognitive approach. The requirements of cognitive plausibility are presented. Then an abduction-based cognitive model of the human deliberative reasoning ability is presented. We believe that implementing such a procedure in a narrative context to generate plans would increase the chances that the characters will be perceived as believable. Our suggestion is that the use of a deliberative reasoning procedure can be used as a basis of several strategies to generate interesting stories.
{"title":"A Cognitive Approach to Narrative Planning with Believable Characters","authors":"Antoine Saillenfest, J. Dessalles","doi":"10.4230/OASIcs.CMN.2014.177","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2014.177","url":null,"abstract":"In this work, we address the question of generating understandable narratives using a cognitive approach. The requirements of cognitive plausibility are presented. Then an abduction-based cognitive model of the human deliberative reasoning ability is presented. We believe that implementing such a procedure in a narrative context to generate plans would increase the chances that the characters will be perceived as believable. Our suggestion is that the use of a deliberative reasoning procedure can be used as a basis of several strategies to generate interesting stories.","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"26 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132655998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-07-28DOI: 10.4230/OASIcs.CMN.2014.164
Rémi Ronfard, N. Szilas
Story generation (including interactive narrative) consists of creating a narrative experience on computer by generating narrative events. It requires building an abstract computational model that can generate a variety of narrative events from a limited set of authored content. These models implement a story logic, as they formalize the occurrence of an event in the story according to various algorithms. At the same time, these stories aim to be expressed to an audience using digital media, which requires a medium logic. In this contribution, we look at the relation between story logic and medium logic in the production of mediated narrative discourse. Using the terminology of Russian formalists and a metaphor borrowed from cinema production, we introduce three models of increasing complexity. In the first model, the story logic (fabulist) creates a fabula which is performed by the medium logic (director) to a screenplay then to the screen. In the second model, the story logic (screenwriter) generates a sjuzhet composed of narrative discourse acts that are staged by the medium logic (director). In the third model, the story and medium logics communicate bidirectionally as co-authors of the screenplay in order to render the story optimally. 1998 ACM Subject Classification H.5.4 Hypertext/Hypermedia: Theory, J.5 Arts and humanities: Literature
{"title":"Where Story and Media Meet: Computer Generation of Narrative Discourse","authors":"Rémi Ronfard, N. Szilas","doi":"10.4230/OASIcs.CMN.2014.164","DOIUrl":"https://doi.org/10.4230/OASIcs.CMN.2014.164","url":null,"abstract":"Story generation (including interactive narrative) consists of creating a narrative experience on computer by generating narrative events. It requires building an abstract computational model that can generate a variety of narrative events from a limited set of authored content. These models implement a story logic, as they formalize the occurrence of an event in the story according to various algorithms. At the same time, these stories aim to be expressed to an audience using digital media, which requires a medium logic. In this contribution, we look at the relation between story logic and medium logic in the production of mediated narrative discourse. Using the terminology of Russian formalists and a metaphor borrowed from cinema production, we introduce three models of increasing complexity. In the first model, the story logic (fabulist) creates a fabula which is performed by the medium logic (director) to a screenplay then to the screen. In the second model, the story logic (screenwriter) generates a sjuzhet composed of narrative discourse acts that are staged by the medium logic (director). In the third model, the story and medium logics communicate bidirectionally as co-authors of the screenplay in order to render the story optimally. 1998 ACM Subject Classification H.5.4 Hypertext/Hypermedia: Theory, J.5 Arts and humanities: Literature","PeriodicalId":311534,"journal":{"name":"Workshop on Computational Models of Narrative","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130075266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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