Name
Abstract | ||
|---|---|---|
Named entity disambiguation (NED) is an important stage in Natural Language Processing (NLP) which automatically resolves mentions to entities in a given knowledge base (KB) like Wikipedia. NED is a complex and challenging problem due to the inherent ambiguity between real world mentions and the entities they refer to. Most existing studies use hand-crafted features to represent mentions, context and entities, which is labor intensive. In this paper, we address this problem by presenting a new NED model which combining local, context and global evidence. By leveraging the learned mixed dense word-level and topic-level representations and the graph-based disambiguation approach, context and global features are well captured. Experiments for NED are conducted on AIDA dataset, which show that the proposed model can obtain state-of-the-art results. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/IALP.2017.8300533 | 2017 International Conference on Asian Language Processing (IALP) |
Keywords | Field | DocType |
Named entity disambiguation,topic model,representation learning,graph model | Entity linking,Graph,Computer science,Artificial intelligence,Natural language processing,Knowledge base,Topic model,Ambiguity,Feature learning,Graph model | Conference |
ISSN | ISBN | Citations |
2159-1962 | 978-1-5386-1982-7 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Ma | 1 | 15 | 2.57 |
| Tonghai Jiang | 2 | 1 | 4.75 |
| Yating Yang | 3 | 1 | 5.14 |
| Xi Zhou | 4 | 2 | 6.83 |
| Lei Wang | 5 | 6 | 6.89 |