Name
Abstract | ||
|---|---|---|
Linking words or phrases in unstructured text to entities in knowledge bases is the problem of entity recognition and disambiguation. In this paper, we focus on the task of entity recognition in Web text to address the challenges of entity correctness, completeness and emergence that existing approaches mainly suffer from. Experimental results show that our approach significantly outperforms the state-of-the-art approaches in terms of precision, F-measure, micro-accuracy and macro-accuracy, while still preserving high recall. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2740908.2742766 | WWW (Companion Volume) |
Field | DocType | Citations |
Entity linking,Data mining,Computer science,Correctness,Named entity,Weak entity,Artificial intelligence,Natural language processing,SGML entity,Completeness (statistics),Recall | Conference | 3 |
PageRank | References | Authors |
0.40 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Zhang | 1 | 21 | 2.44 |
| Yunpeng Dong | 2 | 3 | 0.40 |
| Achim Rettinger | 3 | 409 | 36.04 |