A cascade method for detecting hedges and their scope in natural language text

Detecting hedges and their scope in natural language text is very important for information inference. In this paper, we present a system based on a cascade method for the CoNLL-2010 shared task. The system composes of two components: one for detecting hedges and another one for detecting their scope. For detecting hedges, we build a cascade subsystem. Firstly, a conditional random field (CRF) model and a large margin-based model are trained respectively. Then, we train another CRF model using the result of the first phase. For detecting the scope of hedges, a CRF model is trained according to the result of the first subtask. The experiments show that our system achieves 86.36% F-measure on biological corpus and 55.05% F-measure on Wikipedia corpus for hedge detection, and 49.95% F-measure on biological corpus for hedge scope detection. Among them, 86.36% is the best result on biological corpus for hedge detection.