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This paper is concerned with the use of conversational agents as an interaction paradigm for ac-cessing open domain encyclopedic knowledge by means of Wikipedia. More precisely, we describe a dialog-based question answering system for Ger-man which utilizes Wikipedia-based topic models as a reference point for context detection and answer prediction. We(More)
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 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)
This paper presents an approach for predicting context sensitive entities exemplified in the domain of person names. Our approach is based on building a weighted context but also a weighted people graph and predicting the context entity by extracting the best fitting sub graph using a spreading activation technique. The results of the experiments show a(More)
This paper proposes a web-based application which combines social tagging, enhanced visual representation of a document and the alignment to an open-ended social ontology. More precisely we introduce on the one hand an approach for automatic extraction of document related keywords for indexing and representing document content as an alternative to social(More)
Purpose: We present a topic classification model using the Dewey Decimal Classification (DDC) as the target scheme. This is done by exploring metadata as provided by the Open Archives Initiative (OAI) to derive document snippets as minimal document representations. The reason is to reduce the effort of document processing in digital libraries. Further, we(More)
This paper introduces a model harvesting the crowd-sourced encyclopedic knowledge provided by Wikipedia to improve the conversational abilities of an artificial agent. More precisely, we present a model for automatic topic identification in ongoing natural language dialogs. On the basis of a graph-based representation of the Wikipedia category system, our(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)