292 research outputs found
Introducing the diagrammatic semiotic mode
Peer reviewe
Towards a crowdsourced solution for the authoring bottleneck in interactive narratives
Interactive Storytelling research has produced a wealth of technologies that can be
employed to create personalised narrative experiences, in which the audience takes
a participating rather than observing role. But so far this technology has not led
to the production of large scale playable interactive story experiences that realise
the ambitions of the field. One main reason for this state of affairs is the difficulty
of authoring interactive stories, a task that requires describing a huge amount of
story building blocks in a machine friendly fashion. This is not only technically
and conceptually more challenging than traditional narrative authoring but also a
scalability problem.
This thesis examines the authoring bottleneck through a case study and a literature
survey and advocates a solution based on crowdsourcing. Prior work has already
shown that combining a large number of example stories collected from crowd workers
with a system that merges these contributions into a single interactive story can be
an effective way to reduce the authorial burden. As a refinement of such an approach,
this thesis introduces the novel concept of Crowd Task Adaptation. It argues that in
order to maximise the usefulness of the collected stories, a system should dynamically
and intelligently analyse the corpus of collected stories and based on this analysis
modify the tasks handed out to crowd workers.
Two authoring systems, ENIGMA and CROSCAT, which show two radically different
approaches of using the Crowd Task Adaptation paradigm have been implemented and
are described in this thesis. While ENIGMA adapts tasks through a realtime dialog
between crowd workers and the system that is based on what has been learned from
previously collected stories, CROSCAT modifies the backstory given to crowd workers
in order to optimise the distribution of branching points in the tree structure that
combines all collected stories. Two experimental studies of crowdsourced authoring
are also presented. They lead to guidelines on how to employ crowdsourced authoring
effectively, but more importantly the results of one of the studies demonstrate the
effectiveness of the Crowd Task Adaptation approach
Semiotically-grounded distant viewing of diagrams : insights from two multimodal corpora
In this article, we argue for the benefits of combining large-scale analyses of visual materials currently pursued within digital humanities with insights from multimodality research, which is an emerging discipline that studies how human communication relies on appropriate combinations of expressive resources. We show that concepts developed within the field of multimodality research provide appropriate metadata schemes for various modes of expression in large corpora and datasets. We illustrate the proposed approach using a common mode of expression, diagrams, and analyse two recent multimodal diagram corpora using statistical and computational methods. Our results suggest that multimodally-motivated metadata schemes can provide a robust foundation for computational analyses of large corpora and datasets. Even if a corpus or dataset is not designed to support full-blown analyses of multimodal communication, our results imply that multimodality theory can still be used to impose tighter analytical control over a variety of visual materials.Peer reviewe
An ontology-based framework for the automated analysis and interpretation of comic books' images
International audienceSince the beginning of the twenty-first century, the cultural industry has been through a massive and historical mutation induced by the rise of digital technologies. The comic books industry keeps looking for the right solution and has not yet produced anything as convincing as the music or movie have. A lot of energy has been spent to transfer printed material to digital supports so far. The specificities of those supports are not always exploited at the best of their capabilities, while they could potentially be used to create new reading conventions. In spite of the needs induced by the large amount of data created since the beginning of the comics history, content indexing has been left behind. It is indeed quite a challenge to index such a composition of textual and visual information. While a growing number of researchers are working on comic books' image analysis from a low-level point of view, only a few are tackling the issue of representing the content at a high semantic level. We propose in this article a framework to handle the content of a comic book, to support the automatic extraction of its visual components and to formalize the semantic of the domain's codes. We tested our framework over two applications: 1) the unsupervised content discovery of comic books' images, 2) its capabilities to handle complex layouts and to produce a respectful browsing experience to the digital comics reader
A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics, and Benchmark Datasets
Machine Reading Comprehension (MRC) is a challenging NLP research field with
wide real world applications. The great progress of this field in recent years
is mainly due to the emergence of large-scale datasets and deep learning. At
present, a lot of MRC models have already surpassed the human performance on
many datasets despite the obvious giant gap between existing MRC models and
genuine human-level reading comprehension. This shows the need of improving
existing datasets, evaluation metrics and models to move the MRC models toward
'real' understanding. To address this lack of comprehensive survey of existing
MRC tasks, evaluation metrics and datasets, herein, (1) we analyzed 57 MRC
tasks and datasets; proposed a more precise classification method of MRC tasks
with 4 different attributes (2) we summarized 9 evaluation metrics of MRC tasks
and (3) 7 attributes and 10 characteristics of MRC datasets; (4) We also
discussed some open issues in MRC research and highlight some future research
directions. In addition, to help the community, we have collected, organized,
and published our data on a companion website(https://mrc-datasets.github.io/)
where MRC researchers could directly access each MRC dataset, papers, baseline
projects and browse the leaderboard.Comment: 59 page
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 SchlĂźsselaufgabe bei der Verarbeitung natĂźrlicher 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 SchlĂźsselfaktor fĂźr 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. FĂźr Tausende von fiktiven Universen wird Wissen aus Suchmaschinen abgefragt â 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 fĂźr fiktive Domänen stammen hauptsächlich aus BĂźchern und von Fans erstellten Inhalten, die spärlich und fehlerbehaftet sind und schwierige Textstrukturen wie Dialoge und Zitate enthalten. Trainingsdaten fĂźr SchlĂźsselaufgaben wie Taxonomie-Induktion, Named Entity Typing oder Relation Extraction sind ebenfalls nicht verfĂźgbar. ⢠Domain-Eigenschaften und Diversität. Fiktive Universen kĂśnnen sehr anspruchsvoll sein und Entitäten, soziale Strukturen und manchmal auch Sprachen enthalten, die sich von der realen Welt vĂśllig unterscheiden. Moderne Methoden zur Wissensextraktion machen Annahmen Ăźber Entity-Class-, Entity-Subclass- und Entity- Entity-Relationen, die fĂźr fiktive Domänen oft ungĂźltig sind. Bei verschiedenen Genres fiktiver Domänen mĂźssen Modelle auch Ăźber 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 BĂźchern) 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) fĂźr 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 ursprĂźnglichen Quellen und ist in der Lage, Taxonomien fĂźr 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 fĂźr populäre fiktive Domänen nutzt ENTYFI die Ăberlappung 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 feinkĂśrnige Entitätstypisierung mit hoher Präzision und Abdeckung durchzufĂźhren. ⢠Der dritte Beitrag ist ein End-to-End-System, genannt KnowFi, um Relationen zwischen Entitäten aus sehr langen Texten wie ganzen BĂźchern 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 fĂźr alle Relationen ist KnowFi in der Lage, Relationen zwischen Entitätspaaren aus langen fiktiven Texten abzuleiten, und Ăźbertrifft die besten frĂźheren Methoden zur Relationsextraktion
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