Semi-supervised method for Improving Stance Classification on Insufficient Labeled Chinese Newspaper

• Main Authors: Yu Ran, 冉昱
• Other Authors: Shou-De Lin
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/94458318292215345926

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 105 === We aim at developing an intelligent program to classify the stance on the Chinese news article on several controversial topics based on the former crawled data. The difficulty in this problem is the insufficient labeled news so that the model cannot learn enough knowledge. Wei-Ming mainly focus on the feature division, feature clustering to reduct the feature dimension and get higher accuracy with supervised method. We aimed at how to make full use of unlabeled data and use deep learning representation vector as feature to get the result beyond the Wei-Ming’s method. We first use paragraph vector as news’ feature and compare them with word feature and dependency feature, then we use the semi-supervised method, that is self-learning and ladder network with paragraph vector feature. We get the better result in topic 2 with self-learning and other 3 topics beyond the Wei-Ming’s method.