中文摘要：

本文提出了一种基于多重冗余标记的CRFs并将其应用于情感分析任务。该方法不仅能够有效地解决有序标记的分类问题，还能够在保证情感分析中各子任务能够使用不同特征的前提下，将情感分析中的主客观分类、褒贬分类和褒贬强弱分类任务统一在一个模型之中，在多个子任务上寻求联合最优，制约分步完成时误差的传播。实验证明，该方法有效地提高了句子情感分析任务的准确率。在理论上，该方法也为基于最大似然训练的算法解决序回归问题提供了一条途径。

英文摘要：

This paper proposes a new method called Multi-redundant-labeled CRFs and applies it on sentence sentiment analysis. This method can not only solve ordinal regression problems effectively, but also obtain global optimal result over multiple cascaded subtasks by merging subjective/objective classification, polarity classification and sentimental strength rating into an integrated model, with each subtask maintaining its own feature types. Experiments on sentiment classification of sentences show a better performance than standard CRFs, and thus validate the effectiveness of this method. Additionally, this method theoretically provides a way to solve ordinal regression problems for the algorithms whose training is based on maximization likelihood estimation.