DFKI-LT - Annotating Opinions in German Political News

Hong Li, Xiwen Cheng, Kristina Adson, Tal Kirshboim, Feiyu Xu
Annotating Opinions in German Political News
5 8th ELRA Conference on Language Resources and Evaluation, Istanbul, Turkey, European Language Resources Association (ELRA), 5/2012,
Accepted for publication

 
This paper presents an approach to construction of an annotated corpus for German political news for the opinion mining task. The annotated corpus has been applied to learn relation extraction rules for extraction of opinion holders, opinion content and classification of polarities. An adapted annotated schema has been developed on top of the state-of-the-art research. Furthermore, a general tool for annotating relations has been utilized for the annotation task. An evaluation of the inter-annotator agreement has been conducted. The rule learning is realized with the help of the minimally supervised machine learning framework DARE.
 
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