Tagging time in prolog : the temporality effect project

This article combines a brief introduction into a particular philosophical theory of "time" with a demonstration of how this theory has been implemented in a Literary Studies oriented Humanities Computing project. The aim of the project was to create a model of text-based time cognition and design customized markup and text analysis tools that help to understand ‘‘how time works’’: more precisely, how narratively organised and communicated information motivates readers to generate the mental image of a chronologically organized world. The approach presented is based on the unitary model of time originally proposed by McTaggart, who distinguished between two perspectives onto time, the so-called A- and B-series. The first step towards a functional Humanities Computing implementation of this theoretical approach was the development of TempusMarker—a software tool providing automatic and semi-automatic markup routines for the tagging of temporal expressions in natural language texts. In the second step we discuss the principals underlying TempusParser—an analytical tool that can reconstruct temporal order in events by way of an algorithm-driven process of analysis and recombination of textual segments during which the "time stamp" of each segment as indicated by the temporal tags is interpreted

Automatic Extraction of Narrative Structure from Long Form Text

Automatic understanding of stories is a long-time goal of artificial intelligence and natural language processing research communities. Stories literally explain the human experience. Understanding our stories promotes the understanding of both individuals and groups of people; various cultures, societies, families, organizations, governments, and corporations, to name a few. People use stories to share information. Stories are told –by narrators– in linguistic bundles of words called narratives. My work has given computers awareness of narrative structure. Specifically, where are the boundaries of a narrative in a text. This is the task of determining where a narrative begins and ends, a non-trivial task, because people rarely tell one story at a time. People don’t specifically announce when we are starting or stopping our stories: We interrupt each other. We tell stories within stories. Before my work, computers had no awareness of narrative boundaries, essentially where stories begin and end. My programs can extract narrative boundaries from novels and short stories with an F1 of 0.65. Before this I worked on teaching computers to identify which paragraphs of text have story content, with an F1 of 0.75 (which is state of the art). Additionally, I have taught computers to identify the narrative point of view (POV; how the narrator identifies themselves) and diegesis (how involved in the story’s action is the narrator) with F1 of over 0.90 for both narrative characteristics. For the narrative POV, diegesis, and narrative level extractors I ran annotation studies, with high agreement, that allowed me to teach computational models to identify structural elements of narrative through supervised machine learning. My work has given computers the ability to find where stories begin and end in raw text. This allows for further, automatic analysis, like extraction of plot, intent, event causality, and event coreference. These tasks are impossible when the computer can’t distinguish between which stories are told in what spans of text. There are two key contributions in my work: 1) my identification of features that accurately extract elements of narrative structure and 2) the gold-standard data and reports generated from running annotation studies on identifying narrative structure

Affective and Dynamic Beam Search for Story Generation

Storytelling's captivating potential makes it a fascinating research area, with implications for entertainment, education, therapy, and cognitive studies. In this paper, we propose Affective Story Generator (AffGen) for generating interesting narratives. AffGen introduces "intriguing twists" in narratives by employing two novel techniques-Dynamic Beam Sizing and Affective Reranking. Dynamic Beam Sizing encourages less predictable, more captivating word choices using a contextual multi-arm bandit model. Affective Reranking prioritizes sentence candidates based on affect intensity. Our empirical evaluations, both automatic and human, demonstrate AffGen's superior performance over existing baselines in generating affectively charged and interesting narratives. Our ablation study and analysis provide insights into the strengths and weaknesses of AffGen.Comment: Accepted at EMNLP-findings 202

Investigating the Use of Word Embeddingsto Estimate Cognitive Interest in Stories

Narrative processing is an important skill to model bothfrom a cognitive science perspective and a computa-tional modeling perspective which applies to intelligentagents. Communication between humans often involvesstorytelling patterns that make the mundane exchange ofinformation more interesting and with proper emphasison important communicative goals. Current narrativegeneration models evaluate their generations basedon either a priori domain semantics (e.g. game statefor an in-game conversation with player agents) orgeneric text quality measures (e.g. coherence). However,in utilizing storytelling as a communicative tool forreal-world interactions, domain-specific approaches failto generalize and text quality measures fail to ensurethat the narrative is perceived as interesting. Hence, suchgeneration needs to consider the cognitive processesinvolved in the perception of narrative. Using theories ofcognitive interest, we present results of an investigationof whether word embeddings (e.g. GloVe (Pennington,Socher, & Manning, 2014)) could be used to model andestimate cognitive interestingness in stories

Good Timing for Computational Models of Narrative Discourse

The temporal order in which story events are presented in discourse can greatly impact how readers experience narrative; however, it remains unclear how narrative systems can leverage temporal order to affect comprehension and experience. We define structural properties of discourse which provide a basis for computational narratologists to reason about good timing, such as when readers learn about event relationships

The Virtual Storyteller: story generation by simulation

The Virtual Storyteller is a multi-agent framework that generates stories based on a concept called emergent narrative. In this paper, we describe the motivation and approach of the Virtual Storyteller, and give an overview of the computational processes involved in the story generation process. We also discuss some of the challenges posed by our chosen approach

“There’s no good excuse for the kind of shit I’ve made up” : a Narratological-empiric investigation into actual readers’ perceptions of unreliability in digital app-novella Pry (2014/2016)

Tässä empiiristä tutkimusotetta ja kirjallisuustieteellistä teoriaa yhdistävässä tutkielmassa tarkastellaan epäluotettavan kerronnan toteutumista Samantha Gormanin ja Danny Cannizzaron digitaalisessa iOS-kirjasovelluksessa Pry. Tutkielmassa argumentoidaan, että puhtaasti teoreettinen kirjallisuustiede tarvitsee välttämättä tuekseen empiiristä lukijatutkimusta, mikäli tavoitteena on tuottaa tietoa kerronnallisten rakenteiden todellisista toteutuvista toiminnan muodoista. Tutkielman kolme päätavoitetta ovat (1) kerronnallisen epäluotettavuuden tarkastelu teoksessa, (2) empiirisen lukijatutkimuksen merkittävyyden osoittaminen sekä (3) tutkielmassa käytettyjen menetelmien kriittinen arviointi ja kehittäminen. Tutkielman teoreettisena viitekehyksenä käytetään kertomusten rakenteiden systemaattiseen tarkasteluun perustuvaa narratologiaa. Tutkielmassa hyödynnetään erityisesti Wayne C. Boothin ja James Phelanin kerronnan epäluotettavuutta kuvaavia teorioita, joita kehitetään edelleen empiiriseen tarkasteluun sopiviksi. Lisäksi tutkielmassa tarkastellaan 64 kirjoittajan tuottamia analyyseja multimodaalisesta Prysta sekä laadullisin että määrällisin menetelmin. Suuri osa aineistosta koostuu yhdysvaltalaisten, kanadalaisten ja uusiseelantilaisten yliopisto-opiskelijoiden kurssisuorituksina julkaistuista blogiteksteistä, mutta aineistoon kuuluu myös ammattimaisten kirjoittajien sekä harrastajien tuottamia analyyseja. Lukijatulkintojen rinnalle tarjotaan tutkielman kirjoittajan oma luenta, jossa Pry tulkitaan kriittisenä, moniäänisyyden mahdollistavana tarkasteluna erinäisten kollektiivien suhteista erityisesti Persianlahden sodassa ja Irakin sodassa, joihin teos kerronnassaan viittaa. Kirjoittajan oman luennan ja muiden lukijatulkintojen väliin muodostuvan jännitteen kautta tutkielma pyrkii osoittamaan, että yksittäisen lukijan valitsema tulkintakehys voi muokata voimakkaasti luennan pohjalta kehitettävää analyysia ja siten kirjallisuustieteellistä teoretisointia. Tutkielmassa havaitaan, että Pryn kerronnalliset elementit voivat tuottaa toisistaan poikkeavia luentoja, minkä vuoksi kerronnan epäluotettavuus näyttäytyy eri lukijoille eri muodossa. Tulkinnat eivät kuitenkaan ole sattumanvaraisia, vaan ne vaikuttavat asettuvan tiettyjen raamien sisään. Tutkielman lopputulema on, että näihin tulkinnallisiin rajapisteisiin tulisi kiinnittää huomiota jatkotutkimuksessa sekä kerronnallisten rakenteiden tarkastelussa ylipäätään

Evolution of modern literature and film

The evolution of complex narratives, such as fictional books or films, is a fairly new area of cultural-evolutionary research. This chapter, first, discusses a theoretical question: in what sense do narratives evolve? Then, it proceeds to describing how content-based, or hedonic, selection influences the evolution of narrative forms – and briefly, the role of several other evolutionary mechanisms, such as drift and accumulation of innovations. Finally, the chapter presents the methods used for studying the evolution of literature and film at a large scale: from manual coding to various computational techniques.Introduction Narratives evolve Hedonic selection of stories Beyond Selection: Drift, Cumulation, Co-evolution Books and films as data Conclusio