Collaborative ranking: A case study on entity linking

Zheng Chen, Heng Ji

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

Abstract

In this paper, we present a new ranking scheme, collaborative ranking (CR). In contrast to traditional non-collaborative ranking scheme which solely relies on the strengths of isolated queries and one stand-alone ranking algorithm, the new scheme integrates the strengths from multiple collaborators of a query and the strengths from multiple ranking algorithms. We elaborate three specific forms of collaborative ranking, namely, micro collaborative ranking (MiCR), macro collaborative ranking (MaCR) and micro-macro collaborative ranking (MiMaCR). Experiments on entity linking task show that our proposed scheme is indeed effective and promising.

Original languageEnglish (US)
Title of host publicationEMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
Pages771-781
Number of pages11
StatePublished - Oct 3 2011
Externally publishedYes
EventConference on Empirical Methods in Natural Language Processing, EMNLP 2011 - Edinburgh, United Kingdom
Duration: Jul 27 2011Jul 31 2011

Publication series

NameEMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Other

OtherConference on Empirical Methods in Natural Language Processing, EMNLP 2011
CountryUnited Kingdom
CityEdinburgh
Period7/27/117/31/11

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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  • Cite this

    Chen, Z., & Ji, H. (2011). Collaborative ranking: A case study on entity linking. In EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 771-781). (EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference).