Ralf Grubenmann


2019

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Correlating Twitter Language with Community-Level Health Outcomes
Arno Schneuwly | Ralf Grubenmann | Séverine Rion Logean | Mark Cieliebak | Martin Jaggi
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task

We study how language on social media is linked to mortal diseases such as atherosclerotic heart disease (AHD), diabetes and various types of cancer. Our proposed model leverages state-of-the-art sentence embeddings, followed by a regression model and clustering, without the need of additional labelled data. It allows to predict community-level medical outcomes from language, and thereby potentially translate these to the individual level. The method is applicable to a wide range of target variables and allows us to discover known and potentially novel correlations of medical outcomes with life-style aspects and other socioeconomic risk factors.

2018

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SB-CH: A Swiss German Corpus with Sentiment Annotations
Ralf Grubenmann | Don Tuggener | Pius von Däniken | Jan Deriu | Mark Cieliebak
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Twist Bytes - German Dialect Identification with Data Mining Optimization
Fernando Benites | Ralf Grubenmann | Pius von Däniken | Dirk von Grünigen | Jan Deriu | Mark Cieliebak
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

We describe our approaches used in the German Dialect Identification (GDI) task at the VarDial Evaluation Campaign 2018. The goal was to identify to which out of four dialects spoken in German speaking part of Switzerland a sentence belonged to. We adopted two different meta classifier approaches and used some data mining insights to improve the preprocessing and the meta classifier parameters. Especially, we focused on using different feature extraction methods and how to combine them, since they influenced very differently the performance of the system. Our system achieved second place out of 8 teams, with a macro averaged F-1 of 64.6%.