Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion

Jiaxin Huang, Yiqing Xie, Yu Meng, Jiaming Shen, Yunyi Zhang, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Given a small set of seed entities (e.g., "USA", "Russia"), corpus-based set expansion is to induce an extensive set of entities which share the same semantic class (Country in this example) from a given corpus. Set expansion benefits a wide range of downstream applications in knowledge discovery, such as web search, taxonomy construction, and query suggestion. Existing corpus-based set expansion algorithms typically bootstrap the given seeds by incorporating lexical patterns and distributional similarity. However, due to no negative sets provided explicitly, these methods suffer from semantic drift caused by expanding the seed set freely without guidance. We propose a new framework, Set-CoExpan, that automatically generates auxiliary sets as negative sets that are closely related to the target set of user's interest, and then performs multiple sets co-expansion that extracts discriminative features by comparing target set with auxiliary sets, to form multiple cohesive sets that are distinctive from one another, thus resolving the semantic drift issue. In this paper we demonstrate that by generating auxiliary sets, we can guide the expansion process of target set to avoid touching those ambiguous areas around the border with auxiliary sets, and we show that Set-CoExpan outperforms strong baseline methods significantly.

Original languageEnglish (US)
Title of host publicationThe Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
PublisherAssociation for Computing Machinery
Number of pages11
ISBN (Electronic)9781450370233
StatePublished - Apr 20 2020
Event29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan, Province of China
Duration: Apr 20 2020Apr 24 2020

Publication series

NameThe Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020


Conference29th International World Wide Web Conference, WWW 2020
Country/TerritoryTaiwan, Province of China


  • Bootstrap Methods
  • Semantic Computing
  • Set Expansion
  • Web Mining

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Cite this