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THUIR at the NTCIR-14 WWW-2 Task

  • Yukun Zheng
  • Zhumin Chu
  • Xiangsheng Li
  • Jiaxin Mao
  • Yiqun LiuEmail author
  • Min Zhang
  • Shaoping Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11966)

Abstract

The THUIR team participated in both Chinese and English subtasks of the NTCIR-14 We Want Web-2 (WWW-2) task. This paper describes our approaches and results in the WWW-2 task. In the Chinese subtask, we designed and trained two neural ranking models on the Sogou-QCL dataset. In the English subtask, we adopted learning to rank models by training them on MQ2007 and MQ2008 datasets. Our methods achieved the best performances in both Chinese and English subtasks. Through further analysis of results, we find that our neural models can achieve better performances in all navigational, informational and transactional queries in Chinese subtask. In the English subtask, the learning-to-rank methods have stronger modeling capabilities than BM25 by learning from effective hand-crafted features.

Keywords

Web search Ad-hoc retrieval Document ranking 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yukun Zheng
    • 1
  • Zhumin Chu
    • 1
  • Xiangsheng Li
    • 1
  • Jiaxin Mao
    • 1
  • Yiqun Liu
    • 1
    Email author
  • Min Zhang
    • 1
  • Shaoping Ma
    • 1
  1. 1.Department of Computer Science and Technology, Institute for Artificial Intelligence, Beijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijingChina

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