| 英文摘要
| Stance news retrieval aims to obtain news, which is related and having the same stance with query, from huge amounts of news. Retrieving news with specific stance can be beneficial, which helps to understand values from different stance, and also helps to analyze the long-term trend of public opinions. We introduce Semi-supervised multi-task learning for stance classification and an re-ranking method for news ranking. The semi-supervised multi-task Learning, a transfer learning method which leverages the structure information in news, significantly outperform the base model without new labeled data. The re-ranking method leverages the relationship between the ranking items, it does not require any human knowledge or any labeled data, improves the ranking performance and is applicable in other ranking task. |
