Entity Disambiguation for Knowledge Base Population

@inproceedings{Dredze2010EntityDF,
  title={Entity Disambiguation for Knowledge Base Population},
  author={Mark Dredze and Paul McNamee and Delip Rao and Adam Gerber and Timothy W. Finin},
  booktitle={COLING},
  year={2010}
}
The integration of facts derived from information extraction systems into existing knowledge bases requires a system to disambiguate entity mentions in the text. This is challenging due to issues such as non-uniform variations in entity names, mention ambiguity, and entities absent from a knowledge base. We present a state of the art system for entity disambiguation that not only addresses these challenges but also scales to knowledge bases with several million entries using very little… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
SHOWING 1-10 OF 196 CITATIONS, ESTIMATED 39% COVERAGE

FILTER CITATIONS BY YEAR

2010
2019

CITATION STATISTICS

  • 21 Highly Influenced Citations

  • Averaged 25 Citations per year over the last 3 years

  • 4% Increase in citations per year in 2018 over 2017

References

Publications referenced by this paper.
SHOWING 1-10 OF 22 REFERENCES

Overview of the TAC 2009 knowledge base population track

  • Paul McNamee, Hoa Trang Dang.
  • Text Analysis Conference (TAC).
  • 2009
Highly Influential
9 Excerpts

Scaling Wikipedia-based named entity disambiguation to arbitrary web text

  • Anthony Fader, Stephen Soderland, Oren Etzioni.
  • WikiAI09 Workshop at IJCAI .
  • 2009
1 Excerpt

Exploiting lexical and encyclopedic resources for entity disambiguation: Final report

  • Steinberger, Michael Strube, Jian Su, Yannick Versley, Xiaofeng Yang, Michael Wick.
  • Technical report, JHU CLSP Summer Workshop.
  • 2008

Similar Papers

Loading similar papers…