A Cascade Method for Detecting Hedges and their Scope in Natural Language Text
Buzhou Tang, Xiaolong Wang, Xuan Wang, Bo Yuan, Shixi Fan · 2010
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. 1