Alexa Breuing

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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)
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)
The Semantic Web is about to become a rich source of knowledge whose potential will be squandered if it is not accessible for everyone. Intuitive interfaces like conversational agents are needed to better disseminate this knowledge, either on request or even proactively in a context-aware manner. This paper presents work on extending an existing(More)
In order to talk to each other meaningfully, conversational partners utilize different types of conversational knowledge. Due to the fact that speakers often use grammatically incomplete and incorrect sentences in spontaneous language, knowledge about conversational and terminological context turns out to be as much important in language understanding as(More)
Spoken interactions between humans are characterized by coherent sequences of utterances assigning a the-matical structure to the whole conversation. Such coherence and the success of a meaningful and flexible dialog are based on the cognitive ability to be aware of the ongoing conversational topic. This paper presents how to enable such topically coherent(More)
Research results in the field of Question Answering (QA) have shown that the classification of natural language questions significantly contributes to the accuracy of the generated answers. In this paper we present an approach which extends the prevalent question classification techniques by additionally considering further contextual information provided(More)
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