Drawing Elena Ferrante's Profile. Workshop Proceedings, Padova, 7 September 2017

Elena Ferrante is an internationally acclaimed Italian novelist whose real identity has been kept secret by E/O publishing house for more than 25 years. Owing to her popularity, major Italian and foreign newspapers have long tried to discover her real identity. However, only a few attempts have been made to foster a scientific debate on her work. In 2016, Arjuna Tuzzi and Michele Cortelazzo led an Italian research team that conducted a preliminary study and collected a well-founded, large corpus of Italian novels comprising 150 works published in the last 30 years by 40 different authors. Moreover, they shared their data with a select group of international experts on authorship attribution, profiling, and analysis of textual data: Maciej Eder and Jan Rybicki (Poland), Patrick Juola (United States), Vittorio Loreto and his research team, Margherita Lalli and Francesca Tria (Italy), George Mikros (Greece), Pierre Ratinaud (France), and Jacques Savoy (Switzerland). The chapters of this volume report the results of this endeavour that were first presented during the international workshop Drawing Elena Ferrante's Profile in Padua on 7 September 2017 as part of the 3rd IQLA-GIAT Summer School in Quantitative Analysis of Textual Data. The fascinating research findings suggest that Elena Ferrante\u2019s work definitely deserves \u201cmany hands\u201d as well as an extensive effort to understand her distinct writing style and the reasons for her worldwide success

Comprehensive Self-Selected Reading and Student Engagement With the Novel: A Program Evaluation

Not reading (Krashen, 2009) is a phenomenon widely noted in students assigned to read as a part of school curriculum. A solution to the many criticisms and deficits cited in the literature surrounding the practice of not reading may lie in the CSSR (Comprehensive Self-Selected Reading) program chosen for focus in this study. In this high school student-reading program, incoming students are guided through a process of textual self-selection and evaluation in an enthusiastic, engaging, and motivating manner. During an eight-month study duration, thirty-two 10th grade students actively read a total of 24,419 pages collaboratively, and 763.09 pages on average. 41% of the sample population attempted novels considered advanced for 10th graders, in defiance of Lexile rating system flaws which categorize many rigorous novels as low level despite their reputation as staples of high school literature. Examples of academic, enthusiastic, and transformative engagement with reading were noted in concluding student interviews

Knowledge extraction from fictional texts

Knowledge extraction from text is a key task in natural language processing, which involves many sub-tasks, such as taxonomy induction, named entity recognition and typing, relation extraction, knowledge canonicalization and so on. By constructing structured knowledge from natural language text, knowledge extraction becomes a key asset for search engines, question answering and other downstream applications. However, current knowledge extraction methods mostly focus on prominent real-world entities with Wikipedia and mainstream news articles as sources. The constructed knowledge bases, therefore, lack information about long-tail domains, with fiction and fantasy as archetypes. Fiction and fantasy are core parts of our human culture, spanning from literature to movies, TV series, comics and video games. With thousands of fictional universes which have been created, knowledge from fictional domains are subject of search-engine queries - by fans as well as cultural analysts. Unlike the real-world domain, knowledge extraction on such specific domains like fiction and fantasy has to tackle several key challenges: - Training data: Sources for fictional domains mostly come from books and fan-built content, which is sparse and noisy, and contains difficult structures of texts, such as dialogues and quotes. Training data for key tasks such as taxonomy induction, named entity typing or relation extraction are also not available. - Domain characteristics and diversity: Fictional universes can be highly sophisticated, containing entities, social structures and sometimes languages that are completely different from the real world. State-of-the-art methods for knowledge extraction make assumptions on entity-class, subclass and entity-entity relations that are often invalid for fictional domains. With different genres of fictional domains, another requirement is to transfer models across domains. - Long fictional texts: While state-of-the-art models have limitations on the input sequence length, it is essential to develop methods that are able to deal with very long texts (e.g. entire books), to capture multiple contexts and leverage widely spread cues. This dissertation addresses the above challenges, by developing new methodologies that advance the state of the art on knowledge extraction in fictional domains. - The first contribution is a method, called TiFi, for constructing type systems (taxonomy induction) for fictional domains. By tapping noisy fan-built content from online communities such as Wikia, TiFi induces taxonomies through three main steps: category cleaning, edge cleaning and top-level construction. Exploiting a variety of features from the original input, TiFi is able to construct taxonomies for a diverse range of fictional domains with high precision. - The second contribution is a comprehensive approach, called ENTYFI, for named entity recognition and typing in long fictional texts. Built on 205 automatically induced high-quality type systems for popular fictional domains, ENTYFI exploits the overlap and reuse of these fictional domains on unseen texts. By combining different typing modules with a consolidation stage, ENTYFI is able to do fine-grained entity typing in long fictional texts with high precision and recall. - The third contribution is an end-to-end system, called KnowFi, for extracting relations between entities in very long texts such as entire books. KnowFi leverages background knowledge from 142 popular fictional domains to identify interesting relations and to collect distant training samples. KnowFi devises a similarity-based ranking technique to reduce false positives in training samples and to select potential text passages that contain seed pairs of entities. By training a hierarchical neural network for all relations, KnowFi is able to infer relations between entity pairs across long fictional texts, and achieves gains over the best prior methods for relation extraction.Wissensextraktion ist ein Schlüsselaufgabe bei der Verarbeitung natürlicher Sprache, und umfasst viele Unteraufgaben, wie Taxonomiekonstruktion, Entitätserkennung und Typisierung, Relationsextraktion, Wissenskanonikalisierung, etc. Durch den Aufbau von strukturiertem Wissen (z.B. Wissensdatenbanken) aus Texten wird die Wissensextraktion zu einem Schlüsselfaktor für Suchmaschinen, Question Answering und andere Anwendungen. Aktuelle Methoden zur Wissensextraktion konzentrieren sich jedoch hauptsächlich auf den Bereich der realen Welt, wobei Wikipedia und Mainstream- Nachrichtenartikel die Hauptquellen sind. Fiktion und Fantasy sind Kernbestandteile unserer menschlichen Kultur, die sich von Literatur bis zu Filmen, Fernsehserien, Comics und Videospielen erstreckt. Für Tausende von fiktiven Universen wird Wissen aus Suchmaschinen abgefragt – von Fans ebenso wie von Kulturwissenschaftler. Im Gegensatz zur realen Welt muss die Wissensextraktion in solchen spezifischen Domänen wie Belletristik und Fantasy mehrere zentrale Herausforderungen bewältigen: • Trainingsdaten. Quellen für fiktive Domänen stammen hauptsächlich aus Büchern und von Fans erstellten Inhalten, die spärlich und fehlerbehaftet sind und schwierige Textstrukturen wie Dialoge und Zitate enthalten. Trainingsdaten für Schlüsselaufgaben wie Taxonomie-Induktion, Named Entity Typing oder Relation Extraction sind ebenfalls nicht verfügbar. • Domain-Eigenschaften und Diversität. Fiktive Universen können sehr anspruchsvoll sein und Entitäten, soziale Strukturen und manchmal auch Sprachen enthalten, die sich von der realen Welt völlig unterscheiden. Moderne Methoden zur Wissensextraktion machen Annahmen über Entity-Class-, Entity-Subclass- und Entity- Entity-Relationen, die für fiktive Domänen oft ungültig sind. Bei verschiedenen Genres fiktiver Domänen müssen Modelle auch über fiktive Domänen hinweg transferierbar sein. • Lange fiktive Texte. Während moderne Modelle Einschränkungen hinsichtlich der Länge der Eingabesequenz haben, ist es wichtig, Methoden zu entwickeln, die in der Lage sind, mit sehr langen Texten (z.B. ganzen Büchern) umzugehen, und mehrere Kontexte und verteilte Hinweise zu erfassen. Diese Dissertation befasst sich mit den oben genannten Herausforderungen, und entwickelt Methoden, die den Stand der Kunst zur Wissensextraktion in fiktionalen Domänen voranbringen. • Der erste Beitrag ist eine Methode, genannt TiFi, zur Konstruktion von Typsystemen (Taxonomie induktion) für fiktive Domänen. Aus von Fans erstellten Inhalten in Online-Communities wie Wikia induziert TiFi Taxonomien in drei wesentlichen Schritten: Kategoriereinigung, Kantenreinigung und Top-Level- Konstruktion. TiFi nutzt eine Vielzahl von Informationen aus den ursprünglichen Quellen und ist in der Lage, Taxonomien für eine Vielzahl von fiktiven Domänen mit hoher Präzision zu erstellen. • Der zweite Beitrag ist ein umfassender Ansatz, genannt ENTYFI, zur Erkennung von Entitäten, und deren Typen, in langen fiktiven Texten. Aufbauend auf 205 automatisch induzierten hochwertigen Typsystemen für populäre fiktive Domänen nutzt ENTYFI die Überlappung und Wiederverwendung dieser fiktiven Domänen zur Bearbeitung neuer Texte. Durch die Zusammenstellung verschiedener Typisierungsmodule mit einer Konsolidierungsphase ist ENTYFI in der Lage, in langen fiktionalen Texten eine feinkörnige Entitätstypisierung mit hoher Präzision und Abdeckung durchzuführen. • Der dritte Beitrag ist ein End-to-End-System, genannt KnowFi, um Relationen zwischen Entitäten aus sehr langen Texten wie ganzen Büchern zu extrahieren. KnowFi nutzt Hintergrundwissen aus 142 beliebten fiktiven Domänen, um interessante Beziehungen zu identifizieren und Trainingsdaten zu sammeln. KnowFi umfasst eine ähnlichkeitsbasierte Ranking-Technik, um falsch positive Einträge in Trainingsdaten zu reduzieren und potenzielle Textpassagen auszuwählen, die Paare von Kandidats-Entitäten enthalten. Durch das Trainieren eines hierarchischen neuronalen Netzwerkes für alle Relationen ist KnowFi in der Lage, Relationen zwischen Entitätspaaren aus langen fiktiven Texten abzuleiten, und übertrifft die besten früheren Methoden zur Relationsextraktion

A Meta-Study and Content Analysis of Science Fiction in Computer Science Research

Ph.D

Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval.

Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches

Elementary school learners’ perceptions of graphic novels in the EFL classroom

This thesis focuses on elementary school learners’ perceptions of reading graphic novels in the EFL classroom. More specifically, the thesis reports on findings from research that examined Norwegian 7th grade learners’ perceptions of the advantages and challenges of reading graphic novels in the EFL classroom. Previous studies have shown that reading graphic novels in the EFL classroom can improve reading comprehension, increase motivation and engagement, and lead to the development of language and various literacies. Despite this, few studies explore learners’ perceptions of these books, and none on elementary school learners’ perceptions. Thus, the current study set out to cover this research gap. To address this research gap, the current study employed a mixed methods research design. A two-week reading project was conducted in a 7th grade classroom with 29 learners, where the researcher conducted observations of 22 participating learners. Following the reading project, the participants responded to a survey, and two groups of four learners participated in a focus group interview. The study found that a majority of the learners perceived graphic novels to be enjoyable and fun, as well as easier to read than other books. The learners found that the combination of images and less text made the books more comprehensible, especially for struggling and reluctant learners, as well as educational in terms of language and skills. The books also provided a variation from their usual lessons, which, combined with their enjoyment of graphic novels, might have instilled motivation and a want to read more. In contrast, some of the learners found reading for whole lessons to be too long for reading graphic novels. Likewise, some learners found that the order and structure of the books could be confusing and the language a bit difficult. A few learners also believed they might learn less from reading graphic novels than regular books as they include images and less text. Overall, the learners perceived graphic novels as enjoyable and advantageous despite a few challenges. As such, the current study’s findings imply that graphic novels should be implemented to a greater extent in the EFL classroom as they can lead to a variety of advantages, as perceived by the learners themselves.This thesis focuses on elementary school learners’ perceptions of reading graphic novels in the EFL classroom. More specifically, the thesis reports on findings from research that examined Norwegian 7th grade learners’ perceptions of the advantages and challenges of reading graphic novels in the EFL classroom. Previous studies have shown that reading graphic novels in the EFL classroom can improve reading comprehension, increase motivation and engagement, and lead to the development of language and various literacies. Despite this, few studies explore learners’ perceptions of these books, and none on elementary school learners’ perceptions. Thus, the current study set out to cover this research gap. To address this research gap, the current study employed a mixed methods research design. A two-week reading project was conducted in a 7th grade classroom with 29 learners, where the researcher conducted observations of 22 participating learners. Following the reading project, the participants responded to a survey, and two groups of four learners participated in a focus group interview. The study found that a majority of the learners perceived graphic novels to be enjoyable and fun, as well as easier to read than other books. The learners found that the combination of images and less text made the books more comprehensible, especially for struggling and reluctant learners, as well as educational in terms of language and skills. The books also provided a variation from their usual lessons, which, combined with their enjoyment of graphic novels, might have instilled motivation and a want to read more. In contrast, some of the learners found reading for whole lessons to be too long for reading graphic novels. Likewise, some learners found that the order and structure of the books could be confusing and the language a bit difficult. A few learners also believed they might learn less from reading graphic novels than regular books as they include images and less text. Overall, the learners perceived graphic novels as enjoyable and advantageous despite a few challenges. As such, the current study’s findings imply that graphic novels should be implemented to a greater extent in the EFL classroom as they can lead to a variety of advantages, as perceived by the learners themselves

The Power of Graphic Novels in EFL Classroom: A proposal including MAUS A Survivor's Tale and PBLL in a 4th year of ESO

Este trabajo ofrece una propuesta didáctica innovadora que implemente la novela gráfica de Art Spiegelman MAUS A Survivor’s Tale (1996) como herramienta pedagógica principal en la asignatura de inglés como segunda lengua en una clase de 4º de la ESO. La razón de ser de este ensayo emerge debido al deseo de demostrar que la literatura puede llegar a ser una herramienta pedagógica útil que ayude a potenciar el pensamiento crítico y la reflexión personal en las clases de EFL. Para demostrar dicha teoría, la primera parte de este trabajo trata de explicar los diferentes pilares teóricos que componen la unidad didáctica: el Enfoque Comunicativo, el Aprendizaje por Proyectos y la importancia de la literatura y, más concretamente, de la novela gráfica en la enseñanza del inglés. Asimismo, la segunda parte del trabajo consiste en exponer el análisis crítico de la propuesta didáctica, así como la relevancia de dicha propuesta en concordancia con los objetivos, las competencias claves y los contenidos específicos estipulados por el Currículo Aragonés y la LOMCE. En relación al marco legislativo, el trabajo busca exponer la relevancia de las actividades propuestas en lo que respecta al trabajo cooperativo y la atención a la diversidad.<br /

The Influence Of Television And Film On Interest In Space And Science

Entertainment media has the great potential to inspire interest in the topics it presents. The purpose of this study is to better understand how entertainment media contributes to people\u27s interests in space and science. There is a huge variety of science communication topics in previous literature, some of which deals with television and film, but very little that specifically study how television and film can inspire interest. A historical review of pioneers in the space industry shows that many were inspired by entertainment media, which at the time consisted of science fiction novels and magazines. In order to explore the possible relationships among influences for scientists and non-scientists and to determine specific questions for future research, I created and distributed an anonymous, online survey. The survey is suggestive, exploratory research using a convenience sampling method and is not meant to provide scientifically accurate statistics. 251 participants completed the survey; 196 were scientists and 55 were non-scientists. The survey showed that the participants did identify entertainment media as a major influencing factor, on a comparable level as factors such as classes or family members. Participants in space-related fields were influenced by entertainment media more than the participants in other fields were. I identified several questions for future research, such as: Are people in space-related fields inspired by entertainment media more than other scientists are? Are non-space-related scientists often inspired by space-related media? Do people who regularly watch science fiction tend to be more scientifically literate than average

Digital Methods in the Humanities: Challenges, Ideas, Perspectives

Digital Humanities is a transformational endeavor that not only changes the perception, storage, and interpretation of information but also of research processes and questions. It also prompts new ways of interdisciplinary communication between humanities scholars and computer scientists. This volume offers a unique perspective on digital methods for and in the humanities. It comprises case studies from various fields to illustrate the challenge of matching existing textual research practices and digital tools. Problems and solutions with and for training tools as well as the adjustment of research practices are presented and discussed with an interdisciplinary focus

Digital Methods in the Humanities

Digital Humanities is a transformational endeavor that not only changes the perception, storage, and interpretation of information but also of research processes and questions. It also prompts new ways of interdisciplinary communication between humanities scholars and computer scientists. This volume offers a unique perspective on digital methods for and in the humanities. It comprises case studies from various fields to illustrate the challenge of matching existing textual research practices and digital tools. Problems and solutions with and for training tools as well as the adjustment of research practices are presented and discussed with an interdisciplinary focus