Title :
Feature-based approach combined with hierarchical classifying strategy to relation extraction
Author :
Qiu, Jing ; Jun-Kang Hao
Author_Institution :
Dept. of Inf. Sci. & Eng., Hebei Univ. of Sci. & Technol., Shijiazhuang, China
Abstract :
This paper proposes a novel feature-based method for relation extraction task. Diverse lexical and syntactic features are defined to describe the context of the pair of entities. Dependency features are selected to capture the structure and dependency information of sentence. Hierarchical classifying strategy is used to reduce the weakness of the traditional approach, which treats training examples in different classes equally and independently, At the same time, correction mechanism is used to improve the performance of the system.
Keywords :
learning (artificial intelligence); ontologies (artificial intelligence); pattern classification; correction mechanism; dependency features; feature-based approach; hierarchical classification strategy; lexical feature; relation extraction task; syntactic feature; Variable speed drives; correction mechanism; dependency tree; hierarchical classifying strategy; relation extraction;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
DOI :
10.1109/ICMLC.2010.5580642