Less is more : completing narratives in miniature fiction

This essay examines how readers interpret and interact with miniature fiction by completing the narratives in these extremely short stories. This is not to suggest that more traditional short stories have always provided complete narratives, but what we have found with miniature fiction is that the reader is more often required to complete the narrative in order for the story to make sense. At the same time, this inferencing process makes readers respond to these stories as they would to texts belonging to other genres. Specifically, we will consider the following pieces of writing: an untitled 6-word story by Graham Swift, ‘The Kids Are Alright’ (148 words) by David Gaffney, ‘Water’ (186 words) by Fred Leebron, and ‘Sparkles’ (175 words) by Louise Yeiser. We have chosen these texts because in our opinion each provides a striking illustration of what ‘short shorts’ require of the reader in order for them to make sense. It could be argued that each text demands more of its readers than the previous; hence the order of our discussion is incremental in terms of the complexity of the texts in question. Common to all four texts are the following: • Inferences made to comprehend the narrative • Inferences employed from known social narratives • Inferences of the types used in reading texts from other genres. Within this general examination of inferences, factors specific to each text will also be analysed

Extracting Narrative Patterns in Different Textual Genres: A Multilevel Feature Discourse Analysis

We present a data-driven approach to discover and extract patterns in textual genres with the aim of identifying whether there is an interesting variation of linguistic features among different narrative genres depending on their respective communicative purposes. We want to achieve this goal by performing a multilevel discourse analysis according to (1) the type of feature studied (shallow, syntactic, semantic, and discourse-related); (2) the texts at a document level; and (3) the textual genres of news, reviews, and children’s tales. To accomplish this, several corpora from the three textual genres were gathered from different sources to ensure a heterogeneous representation, paying attention to the presence and frequency of a series of features extracted with computational tools. This deep analysis aims at obtaining more detailed knowledge of the different linguistic phenomena that directly shape each of the genres included in the study, therefore showing the particularities that make them be considered as individual genres but also comprise them inside the narrative typology. The findings suggest that this type of multilevel linguistic analysis could be of great help for areas of research within natural language processing such as computational narratology, as they allow a better understanding of the fundamental features that define each genre and its communicative purpose. Likewise, this approach could also boost the creation of more consistent automatic story generation tools in areas of language generation.This research work is part of the R&D project “PID2021-123956OB-I00”, funded by MCIN/AEI/10.13039/501100011033/ and by “ERDF A way of making Europe”. Moreover, it was also partially funded by the project “CLEAR.TEXT: Enhancing the modernization public sector organizations by deploying natural language processing to make their digital content CLEARER to those with cognitive disabilities” (TED2021-130707B-I00), by the Generalitat Valenciana through the project “NL4DISMIS: Natural Language Technologies for dealing with dis- and misinformation” with grant reference CIPROM/2021/21, and finally by the European Commission ICT COST Action “Multi-task, Multilingual, Multi-modal Language Generation” (CA18231)

Hide and sneak: story generation with characters that perceive and assume

We describe the design of a perception system for the characters in the Virtual Storyteller (VST), a character-centric story generation system. Previously, these characters were omniscient; stories involving sneaking and deception could not be generated. To remedy this, we limited the characters' visual perception using simple rules. We enabled the characters to make assumptions about the story world, so they can plan toward goals in spite of incomplete knowledge. Using the distinction between the character and actor roles of agents in the VST, we can use the assumptions to steer the story plot

Inducing Stereotypical Character Roles from Plot Structure

If we are to understand stories, we must understand characters: characters are central to every narrative and drive the action forward. Critically, many stories (especially cultural ones) employ stereotypical character roles in their stories for different purposes, including efficient communication among bundles of default characteristics and associations, ease understanding of those characters\u27 role in the overall narrative, and many more. These roles include ideas such as hero, villain, or victim, as well as culturally-specific roles such as, for example, the donor (in Russian tales) or the trickster (in Native American tales). My thesis aims to learn these roles automatically, inducing them from data using a clustering technique. The first step of learning character roles, however, is to identify which coreference chains correspond to characters, which are defined by narratologists as animate entities that drive the plot forward. The first part of my work has focused on this character identification problem, specifically focusing on the problem of animacy detection. Prior work treated animacy as a word-level property, and researchers developed statistical models to classify words as either animate or inanimate. I claimed this approach to the problem is ill-posed and presented a new hybrid approach for classifying the animacy of coreference chains that achieved state-of-the-art performance. The next step of my work is to develop approaches first to identify the characters and then a new unsupervised clustering approach to learn stereotypical roles. My character identification system consists of two stages: first, I detect animate chains from the coreference chains using my existing animacy detector; second, I apply a supervised machine learning model that identifies which of those chains qualify as characters. I proposed a narratologically grounded definition of character and built a supervised machine learning model with a small set of features that achieved state-of-the-art performance. In the last step, I successfully implemented a clustering approach with plot and thematic information to cluster the archetypes. This work resulted in a completely new approach to understanding the structure of stories, greatly advancing the state-of-the-art of story understanding

Cultural Power and Utopianism in Laurie Halse Anderson\u27s Prom and M.T. Anderson\u27s Feed

Author\u27s abstract: Resourcefully and responsibly obtaining a sense of power is central to quality young adult literature. Laurie Halse Anderson\u27s Prom and M.T. Anderson\u27s Feed show their adolescent protagonists\u27 struggles with identity formation, consumerism, and the adult world. In order to address power relationships, the two novels address the rise of a global electronic and print media system that collapses traditional notions of time and space and the excessive consumption associated with the culture such a system creates. However, these two novels explore postmodern consumer culture from different perspectives. Prom functions as a utopian, revisionist fairy tale in which the consequences of rampant consumerism are combated through individual agency and sustained community involvement, whereas Feed acts as an apocalyptic dystopia in which any quest for agency is thwarted by the rampant consumerism connected to the rise of a transnational, info-age economy. The extent to which these two novels fit within the theoretical framework of utopian/dystopian fiction illuminates their disparate approaches to the power struggles associated with the culture industry