Ulli Waltinger

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In this paper, we describe MLSA, a publicly available multi-layered reference corpus for German-language sentiment analysis. The construction of the corpus is based on the manual annotation of 270 German-language sentences considering three different layers of granularity. The sentence-layer annotation, as the most coarse-grained annotation, focuses on(More)
This paper is concerned with the use of conversational agents as an interaction paradigm for accessing open domain encyclopedic knowledge by means of Wikipedia. More precisely, we describe a dialog-based question answering system for German which utilizes Wikipedia-based topic models as a reference point for context detection and answer prediction. We(More)
In the area of digital library services, the access to subjectspecific metadata of scholarly publications is of utmost interest. One of the most prevalent approaches for metadata exchange is the XML-based Open Archive Initiative (OAI) Protocol for Metadata Harvesting (OAIPMH). However, due to its loose requirements regarding metadata content there is no(More)
In recent years a variety of approaches in computing semantic relatedness have been proposed. However, the algorithms and resources employed differ strongly, as well as the results obtained under different experimental conditions. This article investigates the quality of various semantic relatedness measures in a comparative study. We conducted an extensive(More)
This paper introduces eHumanities Desktopan online system for corpus management and analysis in support of Computing in the Humanities. Design issues and the overall architecture are described as well as an initial set of applications which are offered by the system.
In this thesis we analyze the performance of social semantics in textual information retrieval. By means of collaboratively constructed knowledge derived from web-based social networks, inducing both common-sense and domainspecific knowledge as constructed by a multitude of users, we will establish an improvement in performance of selected tasks within(More)
This paper presents an approach using social semantics for the task of topic labelling by means of Open Topic Models. Our approach utilizes a social ontology to create an alignment of documents within a social network. Comprised category information is used to compute a topic generalization. We propose a feature-frequency-based method for measuring semantic(More)