Learning the scope of hedge cues in biomedical texts
Identifying hedged information in biomedical literature is an important subtask in information extraction because it would be misleading to extract speculative information as factual information. In this paper we present a machine learning system that finds the scope of hedge cues in biomedical texts. The system is based on a similar system that finds the scope of negation cues. We show that the same scope finding approach can be applied to both negation and hedging. To investigate the robustness of the approach, the system is tested on the three subcorpora of the BioScope corpus that represent different text types.