Opinion holder identification aims to extract entities
that expressopinions in sentences. In this paper, opinion holderidentification 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.