Content Selection for Effective Counter-Argument Generation

The information ecosystem of social media has resulted in an abundance of opinions on political topics and current events. In order to encourage better discussions, it is important to promote high-quality responses and relegate low-quality ones. We thus focus on automatically analyzing and generating counter-arguments in response to posts on social media with the goal of providing effective responses. This thesis is composed of three parts. In the first part, we conduct an analysis of arguments. Specifically, we first annotate discussions from Reddit for aspects of arguments and then analyze them for their persuasive impact. Then we present approaches to identify the argumentative structure of these discussions and predict the persuasiveness of an argument. We evaluate each component independently using automatic or manual evaluations and show significant improvement in each. In the second part, we leverage our discoveries from our analysis in the process of generating counter-arguments. We develop two approaches in the retrieve-and-edit framework, where we obtain content using methods created during our analysis of arguments, among others, and then modify the content using techniques from natural language generation. In the first approach, we develop an approach to retrieve counter-arguments by annotating a dataset for stance and building models for stance prediction. Then we use our approaches from our analysis of arguments to extract persuasive argumentative content before modifying non-content phrases for coherence. In contrast, in the second approach we create a dataset and models for modifying content -- making semantic edits to a claim to have a contrasting stance. We evaluate our approaches using intrinsic automatic evaluation of our predictive models and an overall human evaluation of our generated output. Finally, in the third part, we discuss the semantic challenges of argumentation that we need to solve in order to make progress in the understanding of arguments. To clarify, we develop new methods for identifying two types of semantic relations -- causality and veracity. For causality, we build a distant-labeled dataset of causal relations using lexical indicators and then we leverage features from those indicators to build predictive models. For veracity, we build new models to retrieve evidence given a claim and predict whether the claim is supported by that evidence. We also develop a new dataset for veracity to illuminate the areas that need progress. We evaluate these approaches using automated and manual techniques and obtain significant improvement over strong baselines. Finally, we apply these techniques to claims in the domain of household electricity consumption, mining claims using our methods for causal relations and then verifying their truthfulness

The building and application of a semantic platform for an e-research society

This thesis reviews the area of e-Research (the use of electronic infrastructure to support research) and considers how the insight gained from the development of social networking sites in the early 21st century might assist researchers in using this infrastructure. In particular it examines the myExperiment project, a website for e-Research that allows users to upload, share and annotate work flows and associated files, using a social networking framework. This Virtual Organisation (VO) supports many of the attributes required to allow a community of users to come together to build an e-Research society. The main focus of the thesis is how the emerging society that is developing out of my-Experiment could use Semantic Web technologies to provide users with a significantly richer representation of their research and research processes to better support reproducible research. One of the initial major contributions was building an ontology for myExperiment. Through this it became possible to build an API for generating and delivering this richer representation and an interface for querying it. Having this richer representation it has been possible to follow Linked Data principles to link up with other projects that have this type of representation. Doing this has allowed additional data to be provided to the user and has begun to set in context the data produced by myExperiment. The way that the myExperiment project has gone about this task and consideration of how changes may affect existing users, is another major contribution of this thesis. Adding a semantic representation to an emergent e-Research society like myExperiment,has given it the potential to provide additional applications. In particular the capability to support Research Objects, an encapsulation of a scientist's research or research process to support reproducibility. The insight gained by adding a semantic representation to myExperiment, has allowed this thesis to contribute towards the design of the architecture for these Research Objects that use similar Semantic Web technologies. The myExperiment ontology has been designed such that it can be aligned with other ontologies. Scientific Discourse, the collaborative argumentation of different claims and hypotheses, with the support of evidence from experiments, to construct, confirm or disprove theories requires the capability to represent experiments carried out in silico. This thesis discusses how, as part of the HCLS Scientific Discourse subtask group, the myExperiment ontology has begun to be aligned with other scientific discourse ontologies to provide this capability. It also compares this alignment of ontologies with the architecture for Research Objects. This thesis has also examines how myExperiment's Linked Data and that of other projects can be used in the design of novel interfaces. As a theoretical exercise, it considers how this Linked Data might be used to support a Question-Answering system, that would allow users to query myExperiment's data in a more efficient and user-friendly way. It concludes by reviewing all the steps undertaken to provide a semantic platform for an emergent e-Research society to facilitate the sharing of research and its processes to support reproducible research. It assesses their contribution to enhancing the features provided by myExperiment, as well as e-Research as a whole. It considers how the contributions provided by this thesis could be extended to produce additional tools that will allow researchers to make greater use of the rich data that is now available, in a way that enhances their research process rather than significantly changing it or adding extra workload

Gamifying Language Resource Acquisition

PhD ThesisNatural Language Processing, is an important collection of methods for processing the vast amounts of available natural language text we continually produce. These methods make use of supervised learning, an approach that learns from large amounts of annotated data. As humans, we’re able to provide information about text that such systems can learn from. Historically, this was carried out by small groups of experts. However, this did not scale. This led to various crowdsourcing approaches being taken that used large pools of non-experts. The traditional form of crowdsourcing was to pay users small amounts of money to complete tasks. As time progressed, gamification approaches such as GWAPs, showed various benefits over the micro-payment methods used before. These included a cost saving, worker training opportunities, increased worker engagement and potential to far exceed the scale of crowdsourcing. While these were successful in domains such as image labelling, they struggled in the domain of text annotation, which wasn’t such a natural fit. Despite many challenges, there were also clearly many opportunities and benefits to applying this approach to text annotation. Many of these are demonstrated by Phrase Detectives. Based on lessons learned from Phrase Detectives and investigations into other GWAPs, in this work, we attempt to create full GWAPs for NLP, extracting the benefits of the methodology. This includes training, high quality output from non-experts and a truly game-like GWAP design that players are happy to play voluntarily

CLARIN

The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium

Performing the digital: performativity and performance studies in digital cultures

How is performativity shaped by digital technologies - and how do performative practices reflect and alter techno-social formations? "Performing the Digital" explores, maps and theorizes the conditions and effects of performativity in digital cultures. Bringing together scholars from performance studies, media theory, sociology and organization studies as well as practitioners of performance, the contributions engage with the implications of digital media and its networked infrastructures for modulations of affect and the body, for performing cities, protest, organization and markets, and for the performativity of critique. With contributions by Marie-Luise Angerer, Timon Beyes, Scott deLahunta and Florian Jenett, Margarete Jahrmann, Susan Kozel, Ann-Christina Lange, Oliver Leistert, Martina Leeker, Jon McKenzie, Sigrid Merx, Melanie Mohren and Bernhard Herbordt, Imanuel Schipper and Jens Schröter

Textual Assemblages and Transmission: Unified models for (Digital) Scholarly Editions and Text Digitisation

Scholarly editing and textual digitisation are typically seen as two distinct, though related, fields. Scholarly editing is replete with traditions and codified practices, while the digitisation of text-bearing material is a recent enterprise, governed more by practice than theory. From the perspective of scholarly editing, the mere digitisation of text is a world away from the intellectual engagement and rigour on which textual scholarship is founded. Recent developments have led to a more open-minded perspective. As scholarly editing has made increasing use of the digital medium, and textual digitisation begins to make use of scholarly editing tools and techniques, the more obvious distinctions dissolve. Such criteria as ‘critical engagement’ become insufficient grounds on which to base a clear distinction. However, this perspective is not without its risks either. It perpetuates the idea that a (digital) scholarly edition and a digitised text are interchangeable. This thesis argues that a real distinction can be drawn. It starts by considering scholarly editing and textual digitisation as textual transmissions. Starting from the ontological perspective of Deleuze and Guattari, it builds a framework capable for considering the processes behind scholarly editing and digitisation. In doing so, it uncovers a number of critical distinction. Scholarly editing creates a regime of representation that is self-consistent and self-validating. Textual digitisation does not. In the final chapters, this thesis uses the crowd-sourced Letters of 1916 project as a test-case for a new conceptualisation of a scholarly edition: one that is neither globally self-consistent nor self-validating, but which provides a conceptual model in which these absences might be mitigated against and the function of a scholarly edition fulfilled

Engineering Agile Big-Data Systems

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse