ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (9): 1987-1999.doi: 10.7544/issn1000-1239.2018.20180133

所属专题: 2018优青专题

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  1. (中国科学院网络数据科学与技术重点实验室(中国科学院计算技术研究所) 北京 100190) (中国科学院计算技术研究所 北京 100190) (
  • 出版日期: 2018-09-01
  • 基金资助: 
    国家自然科学基金优秀青年科学基金项目(61722211) This work was supported by the National Natural Science Foundation of China for Excellent Young Scientists (61722211).

Exploration on Neural Information Retrieval Framework

Guo Jiafeng, Fan Yixing   

  1. (CAS Key Laboratory of Network Data Science & Technology(Institute of Computing Technology, Chinese Academy of Sciences), Beijing 100190) (Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190)
  • Online: 2018-09-01

摘要: 经过几十年的发展,信息检索技术获得了长足的进步和广泛的应用,但当前主流的搜索引擎系统距离真正智能的信息获取系统仍然有较大差距.智能信息获取系统能够对网络大数据的内容进行获取、阅读和理解,对关键语义信息实现存储和检索,并能够依据用户的信息需求进行推理、决策和信息生成.实现这样的系统,迫切需要在检索架构和检索模型上形成根本性的改变和理论突破.近年来,围绕智能信息获取的需求,利用深度学习检索框架展开了系统性研究,在数据表征、数据索引以及检索算法等方向上形成了一系列原创成果,在探索全新的深度学习检索架构上不断迈进.

关键词: 信息检索, 深度学习, 数据表征, 相关匹配, 数据索引

Abstract: After decades of research, information retrieval technology has been significantly advanced and widely applied in our daily life. However, there is still a huge gap between modern search engines and true intelligent information accessing systems. In our opinion, an intelligent information accessing system should be able to crawl, read and understand the content of the big Web data, index and search the key semantic information, and reason, decide and generate the right results based on users’ information need. To develop such kind of systems, we need theoretical breakthrough on the search architecture and models. In recent years, to address the intelligent information accessing problem, we have conducted systematical research on neural information retrieval framework. We have achieved a few of original contributions on text representation, data indexing and relevance matching. However, there is still a long way in this direction and we will continue our exploration on neural information retrieval in the future.

Key words: information retrieval, deep learning, data representation, relevance matching, data indexing