Identify Sentiment-Objects from Chinese Sentences Based on Cascaded Conditional Random Fields
Guolong Chen · Zhongwen xinxi xuebao · 2013
Sentiment-objects extraction aims to identify the targets of opinion described in sentiment sentences.However,previous researches fail to extract compound targets and unknown words.In this paper,the cascaded CRFs model is presented to deal with the problem.The method first acquires opinion target set using lower-lever CRFs model.then,middle-lever models is employed to get candidate set by filtering noise,complementing missing candidate targets,and merging compound noun phrases.Finally,opinion targets set is extract from the higher-lever model using middle-lever model candidate set as input.Experiments show that our method outperforms linear chain CRFs by 1.62% in precision,5.75% in recall,and 4.17% in F1 measure.Meanwhile,the method is also effective to identify the compound targets and unknown targets.