Titlebook: Experimental IR Meets Multilinguality, Multimodality, and Interaction; 8th International Co Gareth J.F. Jones,Séamus Lawless,Nicola Ferro C

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https://doi.org/10.1007/978-3-662-38465-7in online and social media fora. In spite of this, current Question Answering (QA) systems do not support CM and are only designed to work with a single interaction language. This assumption makes it inconvenient for multi-lingual users to interact naturally with the QA system especially in scenario

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Gaussian Network Model of Global Seismicity,users’ stance towards BREXIT, carried out by content analysis of Twitter messages, and the exploration of their social relations, by relying on social network analysis. The analysis of a novel Twitter corpus on the BREXIT debate, developed for our purposes, shows that like-minded individuals (sharin

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Hans-Diether Dahm,Horst Pietznerlled ., based on a set of external (impostor) documents and a random subspace ensemble, is one of the most successful approaches. Variations of this method gained top-performing positions in recent PAN evaluation campaigns. In this paper, we propose a modification of the . method that focuses on bot

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Einleitung und Aufgabenstellung,e tasks of authorship attribution, author profiling, and discriminating between similar languages. We developed a single multi-labeled corpus for the three aforementioned tasks, composed of news articles in different varieties of Spanish. We used the same machine-learning algorithm, Liblinear SVM, i

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Hans-Jürgen Voigt,Thomas WippermannThis paper describes principles for evaluation metrics for lexical components and an implementation of them based on requirements from practical information systems.

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https://doi.org/10.1007/978-3-319-65813-1artificial intelligence; blogs; classification; cluster analysis; clustering algorithms; image segmentati