Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

Enriching unstructured media content about events to enable semi-automated summaries, compilations, and improved search by leveraging social networks

(i) Mobile devices and social networks are omnipresent Mobile devices such as smartphones, tablets, or digital cameras together with social networks enable people to create, share, and consume enormous amounts of media items like videos or photos both on the road or at home. Such mobile devices "by pure definition" accompany their owners almost wherever they may go. In consequence, mobile devices are omnipresent at all sorts of events to capture noteworthy moments. Exemplary events can be keynote speeches at conferences, music concerts in stadiums, or even natural catastrophes like earthquakes that affect whole areas or countries. At such events" given a stable network connection" part of the event-related media items are published on social networks both as the event happens or afterwards, once a stable network connection has been established again. (ii) Finding representative media items for an event is hard Common media item search operations, for example, searching for the official video clip for a certain hit record on an online video platform can in the simplest case be achieved based on potentially shallow human-generated metadata or based on more profound content analysis techniques like optical character recognition, automatic speech recognition, or acoustic fingerprinting. More advanced scenarios, however, like retrieving all (or just the most representative) media items that were created at a given event with the objective of creating event summaries or media item compilations covering the event in question are hard, if not impossible, to fulfill at large scale. The main research question of this thesis can be formulated as follows. (iii) Research question "Can user-customizable media galleries that summarize given events be created solely based on textual and multimedia data from social networks?" (iv) Contributions In the context of this thesis, we have developed and evaluated a novel interactive application and related methods for media item enrichment, leveraging social networks, utilizing the Web of Data, techniques known from Content-based Image Retrieval (CBIR) and Content-based Video Retrieval (CBVR), and fine-grained media item addressing schemes like Media Fragments URIs to provide a scalable and near realtime solution to realize the abovementioned scenario of event summarization and media item compilation. (v) Methodology For any event with given event title(s), (potentially vague) event location(s), and (arbitrarily fine-grained) event date(s), our approach can be divided in the following six steps. 1) Via the textual search APIs (Application Programming Interfaces) of different social networks, we retrieve a list of potentially event-relevant microposts that either contain media items directly, or that provide links to media items on external media item hosting platforms. 2) Using third-party Natural Language Processing (NLP) tools, we recognize and disambiguate named entities in microposts to predetermine their relevance. 3) We extract the binary media item data from social networks or media item hosting platforms and relate it to the originating microposts. 4) Using CBIR and CBVR techniques, we first deduplicate exact-duplicate and near-duplicate media items and then cluster similar media items. 5) We rank the deduplicated and clustered list of media items and their related microposts according to well-defined ranking criteria. 6) In order to generate interactive and user-customizable media galleries that visually and audially summarize the event in question, we compile the top-n ranked media items and microposts in aesthetically pleasing and functional ways

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Semantic Model Alignment for Business Process Integration

Business process models describe an enterprise’s way of conducting business and in this form the basis for shaping the organization and engineering the appropriate supporting or even enabling IT. Thereby, a major task in working with models is their analysis and comparison for the purpose of aligning them. As models can differ semantically not only concerning the modeling languages used, but even more so in the way in which the natural language for labeling the model elements has been applied, the correct identification of the intended meaning of a legacy model is a non-trivial task that thus far has only been solved by humans. In particular at the time of reorganizations, the set-up of B2B-collaborations or mergers and acquisitions the semantic analysis of models of different origin that need to be consolidated is a manual effort that is not only tedious and error-prone but also time consuming and costly and often even repetitive. For facilitating automation of this task by means of IT, in this thesis the new method of Semantic Model Alignment is presented. Its application enables to extract and formalize the semantics of models for relating them based on the modeling language used and determining similarities based on the natural language used in model element labels. The resulting alignment supports model-based semantic business process integration. The research conducted is based on a design-science oriented approach and the method developed has been created together with all its enabling artifacts. These results have been published as the research progressed and are presented here in this thesis based on a selection of peer reviewed publications comprehensively describing the various aspects

Semantic Interaction in Web-based Retrieval Systems : Adopting Semantic Web Technologies and Social Networking Paradigms for Interacting with Semi-structured Web Data

Existing web retrieval models for exploration and interaction with web data do not take into account semantic information, nor do they allow for new forms of interaction by employing meaningful interaction and navigation metaphors in 2D/3D. This thesis researches means for introducing a semantic dimension into the search and exploration process of web content to enable a significantly positive user experience. Therefore, an inherently dynamic view beyond single concepts and models from semantic information processing, information extraction and human-machine interaction is adopted. Essential tasks for semantic interaction such as semantic annotation, semantic mediation and semantic human-computer interaction were identified and elaborated for two general application scenarios in web retrieval: Web-based Question Answering in a knowledge-based dialogue system and semantic exploration of information spaces in 2D/3D

Natural Sciences in Archaeology and Cultural Heritage

A Special Issue of the international journal Sustainability under the section Sustainability of Culture & Heritage has been made, entitled Natural Sciences in Archaeology and Cultural Heritage. The bridge between science/technology and the humanities (archaeology, anthropology, history of art, and cultural heritage) has formed a well-established interdisciplinary subject with several sub-disciplines; it is growing exponentially, spurred by the fast development of technology in other fields (space exploration, medical, military, and industrial applications). On the other hand, art and culture struggle to survive due to neglect, lack of funding, or the dangers of events such as natural disasters and war. This volume strengthens and exerts the documentation of the sustainability of the issue that arises from the outcome of resulting research and the application of such a duality link. The sustainable dimension emerges from society, education, and economics through the impact of cultural growth, all of which produce a balanced society, in which prosperity, harmony, and development are merged at a sustainable local/regional/national/social level. A wide range of subjects linking the applied natural sciences with archaeology and the cultural heritage of innovative research and applications are presented in this volume

Energy-Efficient Software

The energy consumption of ICT is growing at an unprecedented pace. The main drivers for this growth are the widespread diffusion of mobile devices and the proliferation of datacenters, the most power-hungry IT facilities. In addition, it is predicted that the demand for ICT technologies and services will increase in the coming years. Finding solutions to decrease ICT energy footprint is and will be a top priority for researchers and professionals in the field. As a matter of fact, hardware technology has substantially improved throughout the years: modern ICT devices are definitely more energy efficient than their predecessors, in terms of performance per watt. However, as recent studies show, these improvements are not effectively reducing the growth rate of ICT energy consumption. This suggests that these devices are not used in an energy-efficient way. Hence, we have to look at software. Modern software applications are not designed and implemented with energy efficiency in mind. As hardware became more and more powerful (and cheaper), software developers were not concerned anymore with optimizing resource usage. Rather, they focused on providing additional features, adding layers of abstraction and complexity to their products. This ultimately resulted in bloated, slow software applications that waste hardware resources -- and consequently, energy. In this dissertation, the relationship between software behavior and hardware energy consumption is explored in detail. For this purpose, the abstraction levels of software are traversed upwards, from source code to architectural components. Empirical research methods and evidence-based software engineering approaches serve as a basis. First of all, this dissertation shows the relevance of software over energy consumption. Secondly, it gives examples of best practices and tactics that can be adopted to improve software energy efficiency, or design energy-efficient software from scratch. Finally, this knowledge is synthesized in a conceptual framework that gives the reader an overview of possible strategies for software energy efficiency, along with examples and suggestions for future research