Veselina Mironova


2018

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A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events
Martin Schiersch | Veselina Mironova | Maximilian Schmitt | Philippe Thomas | Aleksandra Gabryszak | Leonhard Hennig
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products
Saskia Schön | Veselina Mironova | Aleksandra Gabryszak | Leonhard Hennig
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2014

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Annotating Relation Mentions in Tabloid Press
Hong Li | Sebastian Krause | Feiyu Xu | Hans Uszkoreit | Robert Hummel | Veselina Mironova
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents a new resource for the training and evaluation needed by relation extraction experiments. The corpus consists of annotations of mentions for three semantic relations: marriage, parent―child, siblings, selected from the domain of biographic facts about persons and their social relationships. The corpus contains more than one hundred news articles from Tabloid Press. In the current corpus, we only consider the relation mentions occurring in the individual sentences. We provide multi-level annotations which specify the marked facts from relation, argument, entity, down to the token level, thus allowing for detailed analysis of linguistic phenomena and their interactions. A generic markup tool Recon developed at the DFKI LT lab has been utilised for the annotation task. The corpus has been annotated by two human experts, supported by additional conflict resolution conducted by a third expert. As shown in the evaluation, the annotation is of high quality as proved by the stated inter-annotator agreements both on sentence level and on relationmention level. The current corpus is already in active use in our research for evaluation of the relation extraction performance of our automatically learned extraction patterns.