Analysis and refinement of cross-lingual entity linking

Authors:
Taylor Cassidy;Heng Ji;Hongbo Deng;Jing Zheng;Jiawei Han
Affiliations:
Computer Science Department and Linguistics Department, Queens College and Graduate Center, City University of New York, New York, NY;Computer Science Department and Linguistics Department, Queens College and Graduate Center, City University of New York, New York, NY;Computer Science Department, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL;SRI International, Menlo Park, CA;Computer Science Department, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL
Venue:
CLEF'12 Proceedings of the Third international conference on Information Access Evaluation: multilinguality, multimodality, and visual analytics
Year:
2012

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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.