Cäcilia Zirn

Also published as: Caecilia Zirn


Lost in Discussion? Tracking Opinion Groups in Complex Political Discussions by the Example of the FOMC Meeting Transcriptions
Cäcilia Zirn | Robert Meusel | Heiner Stuckenschmidt
Proceedings of the International Conference Recent Advances in Natural Language Processing


Analyzing Positions and Topics in Political Discussions of the German Bundestag
Cäcilia Zirn
Proceedings of the ACL 2014 Student Research Workshop

DBpedia Domains: augmenting DBpedia with domain information
Gregor Titze | Volha Bryl | Cäcilia Zirn | Simone Paolo Ponzetto
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present an approach for augmenting DBpedia, a very large ontology lying at the heart of the Linked Open Data (LOD) cloud, with domain information. Our approach uses the thematic labels provided for DBpedia entities by Wikipedia categories, and groups them based on a kernel based k-means clustering algorithm. Experiments on gold-standard data show that our approach provides a first solution to the automatic annotation of DBpedia entities with domain labels, thus providing the largest LOD domain-annotated ontology to date.


Bootstrapping an Unsupervised Approach for Classifying Agreement and Disagreement
Bernd Opitz | Cäcilia Zirn
Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013)


Fine-Grained Sentiment Analysis with Structural Features
Cäcilia Zirn | Mathias Niepert | Heiner Stuckenschmidt | Michael Strube
Proceedings of 5th International Joint Conference on Natural Language Processing


WikiNet: A Very Large Scale Multi-Lingual Concept Network
Vivi Nastase | Michael Strube | Benjamin Boerschinger | Caecilia Zirn | Anas Elghafari
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper describes a multi-lingual large-scale concept network obtained automatically by mining for concepts and relations and exploiting a variety of sources of knowledge from Wikipedia. Concepts and their lexicalizations are extracted from Wikipedia pages, in particular from article titles, hyperlinks, disambiguation pages and cross-language links. Relations are extracted from the category and page network, from the category names, from infoboxes and the body of the articles. The resulting network has two main components: (i) a central, language independent index of concepts, which serves to keep track of the concepts' lexicalizations both within a language and across languages, and to separate linguistic expressions of concepts from the relations in which they are involved (concepts themselves are represented as numeric IDs); (ii) a large network built on the basis of the relations extracted, represented as relations between concepts (more specifically, the numeric IDs). The various stages of obtaining the network were separately evaluated, and the results show a qualitative resource.