加拿大蒙特利尔大学聂建云教授学术报告

报告主题：Integrated Learning of Features and Ranking Function in Information Retrieval

报告专家：聂建云，加拿大蒙特利尔大学 教授

报告时间：2019年4月30日（周二）14:30-16:30

报告地点：麦庐园荟庐楼H302报告厅

报告摘要：

Recently, many deep learning models have been proposed for information retrieval and shown competitive results. They typically aim to learn features either about the contents of the document and the query, or about the interactions between them. However, the existing literature shows that document ranking depends simultaneously on many factors, including both content and interaction features. The integration of both types of neural features has not been extensively studied. In addition, many studies have also shown that the deep neural features cannot replace completely the traditional features, but are complementary. It is thus reasonable to combine deep neural features with traditional features. In this paper, we propose an integrated end-to-end learning framework based on learning-to-rank to learn both neural features and the ranking function simultaneously. The framework also has the flexibility to integrate arbitrary traditional features. Our experiments on public datasets confirm that such an integrated learning strategy is better than separate learning of features and ranking function, and integrating traditional features can further improve the results.

专家介绍：

聂建云，加拿大蒙特利尔大学教授，博士毕业于法国Grenoble大学，一直从事信息检索和自然语言处理的研究和教学工作。在信息检索模型、跨语言信息检索、查询扩展、查询理解等方面有多篇论文发表在SIGIR等会议或期刊上，曾获得SIGIR最佳论文，出版了《跨语言信息检索》等专著。他是多个国际期刊的编辑、SIGIR、CIKM、ACL等顶级国际会议的程序委员会委员，是2011年SIGIR的大会主席。