An evaluation framework for cross-lingual link discovery |
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| Authors: | Ling-Xiang Tang Shlomo Geva Andrew Trotman Yue Xu Kelly Y Itakura |
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| Institution: | 1. Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia;2. Department of Computer Science, University of Otago, Dunedin, New Zealand;3. National Institute of Informatics, Japan |
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| Abstract: | Cross-Lingual Link Discovery (CLLD) is a new problem in Information Retrieval. The aim is to automatically identify meaningful and relevant hypertext links between documents in different languages. This is particularly helpful in knowledge discovery if a multi-lingual knowledge base is sparse in one language or another, or the topical coverage in each language is different; such is the case with Wikipedia. Techniques for identifying new and topically relevant cross-lingual links are a current topic of interest at NTCIR where the CrossLink task has been running since the 2011 NTCIR-9. This paper presents the evaluation framework for benchmarking algorithms for cross-lingual link discovery evaluated in the context of NTCIR-9. |
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| Keywords: | Wikipedia Cross-lingual link discovery Evaluation framework Validation Assessment Evaluation metrics |
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