Name
Abstract | ||
|---|---|---|
In this paper we propose two novel approaches to enhance cross-lingual entity linking (CLEL). One is based on cross-lingual information networks, aligned based on monolingual information extraction, and the other uses topic modeling to ensure global consistency. We enhance a strong baseline system derived from a combination of state-of-the-art machine translation and monolingual entity linking to achieve 11.2% improvement in B-Cubed+ F-measure. Our system achieved highly competitive results in the NIST Text Analysis Conference (TAC) Knowledge Base Population (KBP2011) evaluation. We also provide detailed qualitative and quantitative analysis on the contributions of each approach and the remaining challenges. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33247-0_1 | CLEF |
Keywords | Field | DocType |
nist text analysis conference,cross-lingual entity,competitive result,monolingual entity,monolingual information extraction,strong baseline system,global consistency,cross-lingual information network,knowledge base population,detailed qualitative | Entity linking,Population,Text mining,Information retrieval,Computer science,Machine translation,Information extraction,NIST,Artificial intelligence,Natural language processing,Topic model,Knowledge base | Conference |
Citations | PageRank | References |
4 | 0.38 | 20 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taylor Cassidy | 1 | 187 | 12.48 |
| Heng Ji | 2 | 1544 | 127.27 |
| Hongbo Deng | 3 | 861 | 41.00 |
| Jing Zheng | 4 | 442 | 43.00 |
| Jiawei Han | 5 | 43085 | 3824.48 |