结合ECOC与DS证据理论的多目标识别研究

情感分类任务旨在自动识别文本所表达的情感色彩信息(例如，褒或者贬、支持或者反对)。提出一种基于情 绪词与情感词协作学习的情感分类方法:在基于传统情感词资源的基础上，引入少量情绪词辅助学习，只利用大规模 未标注数据实现情感分类。具体来讲，基于文档一单词二部图的标签传播算法框架，利用情绪词与情感词构建两个视 图，通过协作学习的方法从大规模未标注语料中抽取高正确率的自动标注样本作为训练数据，然后训练分类器进行情 感分类。实验表明，该方法在多个领域的情感分类任务中都取得了较好的分类效果。

Multi-class Target Recognition Based on Error-correcting Output Codes and DS Evidence Theory

Sentiment classification aims to distinguish the expressed sentiment categories by the text, such as positive vs. negative and agree vs. disagree. We used a opinion lexicon, together with a small scale of emotion key words to con- duct sentiment classification with unlabeled data. Specifically, a document word bipartite graph was builts, and then the opinion words and emotion words were served as labeled points while the documents were regarded as unlabeled points in the graph. Label propagation algorithm was used to propagate the label information of the words to the documents. Finally, the high confident automatically-labeled samples were used as training data for sentiment classification through collaborative learning method. Experimental results demonstrate that our approach achieves a good performance for sen- timent classification across multiple domains.