Name
Abstract | ||
|---|---|---|
The traditional named entity detection (NED) and entity linking (EL) techniques cannot be applied to domain-specific knowledge base effectively. Most of existing techniques just take extracted named entities as the input to the following EL task without considering the interdependency between the NED and EL and how to detect the Fake Named Entities (FNEs). In this paper, we propose a novel approach to jointly model NED and EL for domain-specific knowledge base, facilitating mentions extracted from unstructured data to be accurately matched to uniquely identifiable entities in the given domain-specific knowledge base. We conduct extensive experiments for movie knowledge base by a data set of real-world movie comments, and our experimental results demonstrate that our proposed approach is able to achieve 84.7 % detection precision for NED and 87.5 % linking accuracy for EL respectively, indicating its practical use for domain-specific knowledge base. |
Year | Venue | Field |
|---|---|---|
2016 | DASFAA | Entity linking,Data mining,Computer science,Unstructured data,Named entity,Knowledge base,Database |
DocType | Citations | PageRank |
Conference | 2 | 0.41 |
References | Authors | |
20 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiangtao Zhang | 1 | 4 | 1.48 |
| Juanzi Li | 2 | 2526 | 154.08 |
| Minh Nhut Nguyen | 3 | 1837 | 112.04 |
| Yao Shi | 4 | 124 | 13.96 |
| Junpeng Li | 5 | 58 | 10.20 |
| Zhigang Wang | 6 | 132 | 13.23 |