实体关系提取解析.pdf

Winnow SVM2004 ACE Automatic Content Extraction Winnow SVM F-Score 73.08% 73.27% ACE TP391 A Automatic Entity Relation Extraction Wanxiang Che, Ting Liu, Sheng Li (School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001) Abstract: Entity Relation Extraction is an important research field in Information Extraction. Two kinds of machine learning algorithms, Winnow and Support Vector Machine (SVM), were used to extract entity relation from the training data of ACE (Automatic Content Extraction) Evaluation 2004 automatically. Both of the algorithms need appropriate feature selection. When two words around an entity were selected, the performance of the both algorithms got the peak. The average weighted F-Score of Winnow and SVM algorithms were 73.08% and 73.27% respectively. We can conclude that when the same feature set is used, the performance of different machine learning algorithms get little difference. So we should pay more attention to find better features when we use the automatic learning methods to extract the entity relation. Key words: entity relation extraction; ACE evaluation; feature selection; Information Extraction Entity TIME ORG PER WEAPON PHYS EMP-ORG PER ORG EMP-ORG