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. © 2012 Springer-Verlag.
CITATION STYLE
Cassidy, T., Ji, H., Deng, H., Zheng, J., & Han, J. (2012). Analysis and refinement of cross-lingual entity linking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7488 LNCS, pp. 1–12). https://doi.org/10.1007/978-3-642-33247-0_1
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