【作者】 车万翔； 刘挺； 李生；

【Author】 Wanxiang Che Ting Liu Sheng Li (Information Retrieval Laboratory,School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001)

【机构】 哈尔滨工业大学计算机学院信息检索研究室；

【摘要】 实体关系抽取是信息抽取研究领域中的重要研究课题。本文使用两种基于特征向量的机器学习算法,Winnow 和支持向量机(SVM),在2004年ACE(Automatic Content Extraction)评测的训练数据上进行实体关系抽取实验。两种算法都进行适当的特征选择,当选择每个实体的左右两个词为特征时,达到最好的抽取效果,Winnow 和SVM 算法的加权平均F-Score 分别为73.08%和73.27%。可见在使用相同的特征向量,不同的学习算法进行实体关系的识别时,最终性能差别不大。因此使用自动的方法进行实体关系抽取时,应当集中精力寻找好的特征。更多还原

【Abstract】 Entity Relation Extraction is an important research field in Information Extraction.Two kinds of machinelearning algorithms,Winnow and Support Vector Machine(SVM),were used to extract entity relation from the training dataof ACE(Automatic Content Extraction)Evaluation 2004 automatically.Both of the algorithms need appropriate featureselection.When two words around an entity were selected,the performance of the both algorithms got the peak.The averageweighted F-Score of Winnow and SVM algorithms were 73.08% and 73.27% separately.We can conclude that when thesame feature vector is used,the performance of different machine learning algorithms gets little difference.So we should paymore attention to find better features when we use the automatic learning methods to extract the entity relation.更多还原