Bauhaus University, Weimar

About: Bauhaus University, Weimar is a education organization based out in Weimar, Thüringen, Germany. It is known for research contribution in the topics: Finite element method & Isogeometric analysis. The organization has 1421 authors who have published 2998 publications receiving 104454 citations. The organization is also known as: Bauhaus-Universität Weimar & Hochschule für Architektur und Bauwesen.

A method for predicting swelling pressure of compacted bentonites

Abstract: An approach for predicting swelling pressure of bentonites based on thermodynamic relationships between swelling pressure and suction is presented in this paper. The proposed method requires sorption isotherm data of the bentonites. A series of swelling pressure tests were performed on compacted specimens of bentonite-sand mixtures with different bentonite contents, water contents, and dry densities. The sorption isotherm of the pure bentonite was measured using a chilled-mirror hygrometer. It is found that the method works well for the bentonite-sand mixtures tested. Several published data on bentonites that have been proposed to be used as buffer and sealing material for nuclear waste repository were collected and used to verify the method. The proposed method is found to be also applicable for other bentonites of different types and therefore, can be used to predict swelling pressure of bentonites.

Webis: An Ensemble for Twitter Sentiment Detection

Abstract: We reproduce four Twitter sentiment classification approaches that participated in previous SemEval editions with diverse feature sets. The reproduced approaches are combined in an ensemble, averaging the individual classifiers’ confidence scores for the three classes (positive, neutral, negative) and deciding sentiment polarity based on these averages. The experimental evaluation on SemEval data shows our re-implementations to slightly outperform their respective originals. Moreover, not too surprisingly, the ensemble of the reproduced approaches serves as a strong baseline in the current edition where it is top-ranked on the 2015 test set.

A comparison of language identification approaches on short, query-style texts

Abstract: In a multi-language Information Retrieval setting, the knowledge about the language of a user query is important for further processing. Hence, we compare the performance of some typical approaches for language detection on very short, query-style texts. The results show that already for single words an accuracy of more than 80% can be achieved, for slightly longer texts we even observed accuracy values close to 100%.