In this paper, we present FRDC's system at participating in the cross-lingual entity linking (CLEL) tasks for the NIST Text Analysis Conference (TAC) Knowledge Base Population (KBP2013) track. We propose a joint approach for mention expansion, disambiguation, and clustering. In particular, we adopt a lexicon and rule based method for entity classification, a collaborative acronym expansion method and a heuristic combination ranking method that merged ListNet, SVM ranking with web search engine ranking. The results achieved in the TAC cross-lingual entity linking tasks show that our approach is competitive. Our best run achieves 0.655 in B^3+ F1 measure.
['Qingliang Miao', 'Ruiyu Fang', 'Yao Meng', 'Shu Zhang']
FRDC's Cross-lingual Entity Linking System at TAC 2013