Identification of Opinion Holders

Opinion holder identification aims to extract entities that express opinions in　sentences. In this paper, opinion holder identification is divided into two subtasks:　author’s opinion recognition and opinion holder labeling. Support vector machine　(SVM) is adopted to recognize author’s opinions, and conditional random field　algorithm (CRF) is utilized to label opinion holders. New features are proposed for　both methods. Our method achieves an f-score of 0.734 in the NTCIR7 MOAT task　on the Traditional Chinese side, which is the best performance among results of　machine learning methods proposed by participants, and also it is close to the best　performance of this task. In addition, inconsistent annotations of opinion holders are analyzed, along with the best way to utilize the training instances with　inconsistent annotations being proposed.