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In this paper, we outline our experiments carried out at the TREC Microblog Track 2011. Our system is based on a plain text index extracted from Tweets crawled from twitter.com. This index has been used to retrieve candidate Tweets for the given topics. The resulting Tweets were post-processed and then analyzed using three different approaches: (i) a burst(More)
The PAN 2016 author profiling task is a supervised classification problem on cross-genre documents (tweets, blog and social media posts). Our system makes use of concreteness, sentiment and syntactic information present in the documents. We train a random forest model to identify gender and age of a doc-ument's author. We report the evaluation results(More)
Our system for the PAN 2015 authorship verification challenge is based upon a two step pre-processing pipeline. In the first step we extract different features that observe stylometric properties, grammatical characteristics and pure statistical features. In the second step of our pre-processing we merge all those features into a single meta feature space.(More)
We use mobile sensor data to predict a mobile phone user's semantic place, e.g. at home, at work, in a restaurant etc. Such information can be used to feed context-aware systems, that adapt for instance mobile phone settings like energy saving, connection to Internet, volume of ringtones etc. We consider the task of semantic place prediction as(More)
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