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

Authors:
Buzhou Tang;Xiaolong Wang;Xuan Wang;Bo Yuan;Shixi Fan
Affiliations:
Harbin Institute of Technology, Shenzhen, Guangdong, China;Harbin Institute of Technology, Shenzhen, Guangdong, China;Harbin Institute of Technology, Shenzhen, Guangdong, China;Harbin Institute of Technology, Shenzhen, Guangdong, China;Harbin Institute of Technology, Shenzhen, Guangdong, China
Venue:
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
Year:
2010

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Abstract

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.