Entity Set Search of Scientific Literature: An Unsupervised Ranking Approach

@article{Shen2018EntitySS,
  title={Entity Set Search of Scientific Literature: An Unsupervised Ranking Approach},
  author={J. Shen and Jinfeng Xiao and Xinwei He and Jingbo Shang and Saurabh Sinha and Jiawei Han},
  journal={The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
  year={2018}
}
  • J. Shen, Jinfeng Xiao, +3 authors Jiawei Han
  • Published 2018
  • Computer Science
  • The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval
  • Literature search is critical for any scientific research. Different from Web or general domain search, a large portion of queries in scientific literature search are entity-set queries, that is, multiple entities of possibly different types. Entity-set queries reflect user's need for finding documents that contain multiple entities and reveal inter-entity relationships and thus pose non-trivial challenges to existing search algorithms that model each entity separately. However, entity-set… CONTINUE READING
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