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We present an annotation approach to capturing emotional and cognitive empathy in student-written peer reviews on business models in German. We propose an annotation scheme that allows us to model emotional and cognitive empathy scores based on three types of review components. Also, we conducted an annotation study with three annotators based on 92 student essays to evaluate our annotation scheme. The obtained inter-rater agreement of α = 0.79 for the components and the π = 0.41 for the empathy scores indicate that the proposed annotation scheme successfully guides annotators to a substantial to moderate agreement. Moreover, we trained predictive models to detect the annotated empathy structures and embedded them in an adaptive writing support system for students to receive individual empathy feedback independent of an instructor, time, and location. We evaluated our tool in a peer learning exercise with 58 students and found promising results for perceived empathy skill learning, perceived feedback accuracy, and intention to use. Finally, we present our freely available corpus of 500 empathy-annotated, student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of empathy support systems.

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Digitale Forschungsinstrumente sind disruptive Technologien mit dem Potential, grundlegend die Art und Weise zu verändern, wie wir in den historischen Bildwissenschaften, in der Architektur- und Designgeschichte oder den Material Culture Studies forschen, ja welche Vorstellungen wir von unseren Disziplinen entwerfen. Künstliche Intelligenz und Verfahren maschinellen Lernens werden in den bildbezogenen Digitalen Geisteswissenschaften zum Zweck der Bildanalyse sowie der Klassifizierung visueller Merkmale genutzt. Waren Methoden im Bereich der Bildanalyse lange Zeit von niedrigschwelligen Methoden wie der Bildung von Clustern auf der Grundlage von Farbverteilungen oder lokalen Kontrasten für diesen Bereich bestimmend, zielen Forschungsinstrumente nun mehr darauf ab, Bildmerkmale zu identifizieren, zu klassifizieren oder gar semantisch zu segmentieren. Sie bieten also, anders ausgedrückt, erstmals informatisch halbautomatischen Zugang zur dem Bild eigenen Ebene der Repräsentation. Das Neoclassica-Projekt hat zum Ziel, Forschenden einen Zugang zu Instrumenten und Methoden zu bieten, die eine computergestützte Analyse materieller Kultur, genauer struktureller Merkmale und ästhetischer Formen klassizistischer Artefakte bieten. Wir möchten damit sowohl mehr hypothesen-getriebene, als auch explorative Zugänge zum Medium Bild unterstützen. Gegenwärtig konzentrieren wir uns dabei auf Raumkunst, insbesondere Mobiliar und Innenräume, aber auch Architektur und deren jeweilige bildliche Darstellung. In diesem Beitrag stellen wir unseren Zugang zur Analyse von Zimmerbildern vor, angefangen mit der Klassifizierung in Darstellungen von Einzelartefakten bis hin zur Identifizierung von Objekten in komplexen zeitgenössischen Interieurdarstellungen. Abschließend zeigen wir Anwendungsmöglichkeiten der Technologie und Perspektiven der weiteren Forschung auf.

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get_appThiemo Wambsganss, Christina Niklaus, Matthias Cetto, Matthias Söllner, Siegfried Handschuh, Jan Marco Leimeister
Journal paper
Recent advances in Natural Language Processing (NLP) bear the opportunity to analyze the argumentation quality of texts. This can be leveraged to provide students with individual and adaptive feedback in their personal learning journey. To test if individual feedback on students' argumentation will help them to write more convincing texts, we developed AL, an adaptive IT tool that provides students with feedback on the argumentation structure of a given text. We compared AL with 54 students to a proven argumentation support tool. We found students using AL wrote more convincing texts with better formal quality of argumentation compared to the ones using the traditional approach. The measured technology acceptance provided promising results to use this tool as a feedback application in different learning settings. The results suggest that learning applications based on NLP may have a beneficial use for developing better writing and reasoning for students in traditional learning settings.

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get_appThiemo Wambsganss, Christina Niklaus, Matthias Söllner, Siegfried Handschuh, Jan Marco Leimeister
Conference or Workshop Item
In this paper, we present a novel annotation approach to capture claims and premises of arguments and their relations in student-written persuasive peer reviews on business models in Ger-man language. We propose an annotation scheme based on annotation guidelines that allows to model claims and premises as well as support and attack relations for capturing the structure of argumentative discourse in student-written peer reviews. We conduct an annotation study with three annotators on 50 persuasive essays to evaluate our annotation scheme. The obtained inter-rater agreement of α = 0.57 for argument components and α = 0.49 for argumentative relations indicates that the proposed annotation scheme successfully guides annotators to moderate agreement. Finally, we present our freely available corpus of 1,000 persuasive student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of argumentative writing support systems for students.

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There is some theoretical evidence that deep neural networks with multiple hidden layers have a potential for more efficient representation of multidimensional mappings than shallow networks with a single hidden layer. The question is whether it is possible to exploit this theoretical advantage for finding such representations with help of numerical training methods. Tests using prototypical problems with a known mean square minimum did not confirm this hypothesis. Minima found with the help of deep networks have always been worse than those found using shallow networks. This does not directly contradict the theoretical findings—it is possible that the superior representational capacity of deep networks is genuine while finding the mean square minimum of such deep networks is a substantially harder problem than with shallow ones.

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get_appBernhard Bermeitinger, Sebastian Gassner, Siegfried Handschuh, Gernot Howanitz, Erik Radisch, Malte Rehbein
Journal paper
A large number of digital humanities projects focuses on text. This medial limitation may be attributed to the abundance of well-established quantitative methods applicable to text. Cultural Studies, however, analyse cultural expressions in a broad sense, including different non-textual media, physical artefacts, and performative actions. It is, to a certain extent, possible to transcribe these multi-medial phenomena in textual form; however, this transcription is difficult to automate and some information may be lost. Thus, quantitative approaches which directly access media-specific information are a desideratum for Cultural Studies. Visual media constitute a significant part of cultural production. In our paper, we propose Deep Watching as a way to analyze visual media (films, photographs, and video clips) using cutting-edge machine learning and computer vision algorithms. Unlike previous approaches, which were based on generic information such as frame differences (Howanitz 2015), color distribution (Burghardt/Wolff 2016) or used manual annotation altogether (Dunst/Hartel 2016), Deep Watching allows to automatically identify visual information (symbols, objects, persons, body language, visual configuration of the scene) in large image and video corpora. To a certain extent, Tilton and Arnold’s Distant-Viewing Toolkit uses a comparable approach (Tilton/Arnold 2018). However, by means of our customized training of state-of-the-art convolutional neural networks for object detection and face recognition we can, in comparison to this toolkit, automatically extract more information about individual frames and their contexts.

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We present an approach for recursively splitting and rephrasing complex English sentences into a novel semantic hierarchy of simplified sentences, with each of them presenting a more regular structure that may facilitate a wide variety of artificial intelligence tasks, such as machine translation (MT) or information extraction (IE). Using a set of hand-crafted transformation rules, input sentences are recursively transformed into a two-layered hierarchical representation in the form of core sentences and accompanying contexts that are linked via rhetorical relations. In this way, the semantic relationship of the decomposed constituents is preserved in the output, maintaining its interpretability for downstream applications. Both a thorough manual analysis and automatic evaluation across three datasets from two different domains demonstrate that the proposed syntactic simplification approach outperforms the state of the art in structural text simplification. Moreover, an extrinsic evaluation shows that when applying our framework as a preprocessing step the performance of state-of-the-art Open IE systems can be improved by up to 346% in precision and 52% in recall. To enable reproducible research, all code is provided online.

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Journal paper
Artificial Intelligence models are becoming increasingly more powerful and accurate, supporting or even replacing humans' decision making. But with increased power and accuracy also comes higher complexity, making it hard for users to understand how the model works and what the reasons behind its predictions are. Humans must explain and justify their decisions, and so do the AI models supporting them in this process, making semantic interpretability an emerging field of study. In this work, we look at interpretability from a broader point of view, going beyond the machine learning scope and covering different AI fields such as distributional semantics and fuzzy logic, among others. We examine and classify the models according to their nature and also based on how they introduce interpretability features, analyzing how each approach affects the final users and pointing to gaps that still need to be addressed to provide more human-centered interpretability solutions.

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get_appSimon Donig, Bernhard Bermeitinger, Maria Christoforaki, Siegfried Handschuh
Journal paper
In den letzten Jahren hat die Anwendung von Verfahren der Computer Vision im Bereich der digitalen Kunstgeschichte und Objektforschung erheblich an Bedeutung gewonnen. Dabei stellt das Schließen der semantischen Lücke eine zentrale Herausforderung für (teil-)automatisierte algorithmische Verfahren dar. Hier schlagen wir einen multimodalen Zugang vor, in dem wir eine fruchtbringende Lösung des Problems sehen und den wir im Kontext des Neoclassica-Projekts entwickeln.

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Recognizing textual entailment is a key task for many seman- tic applications, such as Question Answering, Text Summa- rization, and Information Extraction, among others. Entail- ment scenarios can range from a simple syntactic variation to more complex semantic relationships between pieces of text, but most approaches try a one-size-fits-all solution that usually favors some scenario to the detriment of another. We propose a composite approach for recognizing text entailment which analyzes the entailment pair to decide whether it must be resolved syntactically or semantically. We also make the answer interpretable: whenever an entailment is solved se- mantically, we explore a knowledge base composed of structured lexical definitions to generate natural language human- like justifications, explaining the semantic relationship hold- ing between the pieces of text. Besides outperforming well- established entailment algorithms, our composite approach gives an important step towards Explainable AI, using world knowledge to make the semantic reasoning process explicit and understandable.

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We introduce DisSim, a discourse-aware sentence splitting framework for English and German whose goal is to transform syntactically complex sentences into an intermediate representation that presents a simple and more regular structure which is easier to process for downstream semantic applications. For this purpose, we turn input sentences into a two-layered semantic hierarchy in the form of core facts and accompanying contexts, while identifying the rhetorical relations that hold between them. In that way, we preserve the coherence structure of the input and, hence, its interpretability for downstream tasks.

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We compiled a new sentence splitting corpus that is composed of 203K pairs of aligned complex source and simplified target sentences. Contrary to previously proposed text simplification corpora, which contain only a small number of split examples, we present a dataset where each input sentence is broken down into a set of minimal propositions, i.e. a sequence of sound, self-contained utterances with each of them presenting a minimal semantic unit that cannot be further decomposed into meaningful propositions. This corpus is useful for developing sentence splitting approaches that learn how to transform sentences with a complex linguistic structure into a fine-grained representation of short sentences that present a simple and more regular structure which is easier to process for downstream applications and thus facilitates and improves their performance.

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Different semantic interpretation tasks such as text entailment and question answering require the classification of semantic relations between terms or entities within text. However, in most cases it is not possible to assign a direct semantic relation between entities/terms. This paper proposes an approach for composite semantic relation classification using one or more relations between entities/term mentions, extending the traditional seman- tic relation classification task. Different from existing approaches, which use machine learning models built over lexical and distributional word vector features, the proposed model uses the combination of a large commonsense knowledge base of binary relations, a distributional navigational algorithm and sequence classification to provide a solution for the composite semantic relation classification problem. The proposed approach outperformed existing baselines with regard to F1-score, Accuracy, Precision and Recall.

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We present an Open Information Extraction (IE) approach that uses a two-layered transformation stage consisting of a clausal disembedding layer and a phrasal disembedding layer, together with rhetorical relation identification. In that way, we convert sentences that present a complex linguistic structure into simplified, syntactically sound sentences, from which we can extract propositions that are represented in a two-layered hierarchy in the form of core relational tuples and accompanying contextual information which are semantically linked via rhetorical relations. In a comparative evaluation, we demonstrate that our reference implementation Graphene outperforms state-of-the-art Open IE systems in the construction of correct n-ary predicate-argument structures. Moreover, we show that existing Open IE approaches can benefit from the transformation process of our framework.

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Conference or Workshop Item
We provide a detailed overview of the various approaches that were proposed to date to solve the task of Open Information Extraction. We present the major challenges that such systems face, show the evolution of the suggested approaches over time and depict the specific issues they address. In addition, we provide a critique of the commonly applied evaluation procedures for assessing the performance of Open IE systems and highlight some directions for future work.

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The ability to automatically interpret natural language commands and actions has the potential of freeing up end-users to interact with software artefacts without the syntactic, vocabulary and formal constraints of a programming language. As most semantic parsers for end-user programming have been operating under a restricted vocabulary setting, it is unclear how these approaches perform over conditions of high semantic heterogeneity (e.g. in an open vocabulary). As the generation of annotated data is costly and time-consuming, models that effectively address complex learning problems constrained under the assumption of small annotated data sets are highly relevant. In this paper, we propose a semantic parsing approach to map natural language commands to actions from a large and heterogeneous frame set trained under a small set of annotated data. The semantic parsing approach uses the combination of semantic role labelling, distributional semantics geometric features and semantic pivoting in order to address the semantic matching problem in an open vocabulary setting.

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In this demo paper, we present a text simplification approach that is directed at improving the performance of state-of-the-art Open Relation Extraction (RE) systems. As syntactically complex sentences often pose a challenge for current Open RE approaches, we have developed a simplification framework that performs a pre-processing step by taking a single sentence as input and using a set of syntactic-based transformation rules to create a textual input that is easier to process for subsequently applied Open RE systems.

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This short paper outlines research results on object classification in images of Neoclassical furniture. The motivation was to provide an object recognition framework which is able to support the alignment of furniture images with a symbolic level model. A data-driven bottom-up research routine in the Neoclassica research framework is the main use-case. This research framework is described more extensively by Donig et al. [2]. It strives to deliver tools for analyzing the spread of aesthetic forms which are considered as a cultural transfer process.

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