Learning Salient Samples and Distributed Representations for Topic-Based Chinese Message Polarity Classification

We describe our participation in the TopicBased Chinese Message Polarity Classification Task, based on the restricted and unrestricted resources respectively. In the restricted resource based classification, we focus on the selection of parameters in a multi-class classification model with highly-biased training data. In the unrestricted resource based classification, we explore the distributed representation of Chinese words through unsupervised feature learning and the annotation of salient samples through active learning, with a raw corpus of over 90 million messages extracted from Chinese Weibo Platform. For two classification subtasks, our submitted results ranked the 4th and the 2nd respectively.