We would like to thank all the participants for their efforts and inspiring feedback on the shared task. Comments, suggestions and remarks on the shared task are welcome at conll2010st(AT)inf(DOT)u-szeged(DOT)hu.
In Natural Language Processing (NLP) - in particular, in Information Extraction (IE) - many applications aim at extracting factual information from text. In order to distinguish facts from unreliable or uncertain information, linguistic devices such as hedges (indicating that authors do not or cannot back up their opinions/statements with facts) have to be identified. Applications should handle detected speculative parts in a different manner.
Hedge detection has received considerable interest recently in the biomedical NLP community, including research papers addressing the detection of hedge devices in biomedical texts, and some recent work on detecting the in-sentence scope of hedge cues in text. Exploiting the hedge scope annotated BioScope corpus and publicly available Wikipedia weasel annotations, the goals of the Shared Task were
Task 1: learning to detect sentences containing uncertainty and
Task 2: learning to resolve the in-sentence scope of hedge cues.
Veronika Vincze, György Szarvas, Richárd Farkas, György Mora, and János Csirik: The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes. BMC Bioinformatics, 9(Suppl 11):S9, 2008.
Viola Ganter and Michael Strube: Finding hedges by chasing weasels: Hedge detection using wikipedia tags and shallow linguistic features. In Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pages 173-176, Suntec, Singapore, August 2009. Association for Computational Linguistics.
Roser Morante and Walter Daelemans: Learning the scope of hedge cues in biomedical texts. In Proceedings of the BioNLP 2009 Workshop, pages 28-36, Boulder, Colorado, June 2009. Association for Computational Linguistics.
The important dates for the shared task were as follows:
The CoNLL-2010 Shared Task was organised by the Human Language Technology Group, University of Szeged.
Richárd Farkas, Human Language Technology Group, University of Szeged
Veronika Vincze, Human Language Technology Group, University of Szeged
György Szarvas, Ubiquitous Knowledge Processing Lab, Technische Universität Darmstadt
György Móra, Human Language Technology Group, University of Szeged
János Csirik, Research Group of Artificial Intelligence, Hungarian Academy of Sciences