CAJ | 학술논문

条件随机场模型是目前处理We b对象属性标注问题的最佳统计模型。为解决条件随机场模型不能充分利用We b对象和属性标签之间的特征关系这一问题，提出了一种增强约束条件随机场模型。借鉴最大间隔的思想，在原有条件随机场模型中增加约束条件和增强因子以提高模型标注正确率。使用最大似然参数估计方法估计模型特征函数的权重参数，并用Viterbi算法进行预测。在数据集中引入验证集的概念，以获得最优增强因子。实验结果表明，该模型有效地提高了We b对象属性标注正确率。
조건수궤장모형시목전처리We b대상속성표주문제적최가통계모형。위해결조건수궤장모형불능충분이용We b대상화속성표첨지간적특정관계저일문제，제출료일충증강약속조건수궤장모형。차감최대간격적사상，재원유조건수궤장모형중증가약속조건화증강인자이제고모형표주정학솔。사용최대사연삼수고계방법고계모형특정함수적권중삼수，병용Viterbi산법진행예측。재수거집중인입험증집적개념，이획득최우증강인자。실험결과표명，해모형유효지제고료We b대상속성표주정학솔。
Conditional random fields model is one of the best statistical models of attribute labeling for Web objects. To overcome the problem that conditional random fields model does not take full advantage of the relationship between Web objects and attribute labels, this paper proposes a boosted constrained conditional random fields model. Motivated by the maximum margin criterion, the proposed model introduces constraints and boosting factor into the conditional random fields model to improve the accuracy of attribute labeling. Maximum likelihood estimation is used for the weights of characteristic functions and the Viterbi algorithm is applied for labeling. To get the best boosting factor, the concept of validation set is introduced in the dataset. The experimental results show that the labeling accuracy is improved effectively.