Abstract
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 resources. Further, our approach achieves performance of up to 95% on entities mentioned from newswire and 80% on a public test set that was designed to include challenging queries.
| Original language | English (US) |
|---|---|
| Pages | 277-285 |
| Number of pages | 9 |
| State | Published - 2010 |
| Externally published | Yes |
| Event | 23rd International Conference on Computational Linguistics, Coling 2010 - Beijing, China Duration: Aug 23 2010 → Aug 27 2010 |
Conference
| Conference | 23rd International Conference on Computational Linguistics, Coling 2010 |
|---|---|
| Country/Territory | China |
| City | Beijing |
| Period | 8/23/10 → 8/27/10 |
ASJC Scopus subject areas
- Language and Linguistics
- Computational Theory and Mathematics
- Linguistics and Language