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Global Entity Ranking Across Multiple Languages
March 17, 2017 Β· Declared Dead Β· π The Web Conference
"No code URL or promise found in abstract"
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Authors
Prantik Bhattacharyya, Nemanja Spasojevic
arXiv ID
1703.06108
Category
cs.IR: Information Retrieval
Cross-listed
cs.CL,
cs.SI
Citations
5
Venue
The Web Conference
Last Checked
4 months ago
Abstract
We present work on building a global long-tailed ranking of entities across multiple languages using Wikipedia and Freebase knowledge bases. We identify multiple features and build a model to rank entities using a ground-truth dataset of more than 10 thousand labels. The final system ranks 27 million entities with 75% precision and 48% F1 score. We provide performance evaluation and empirical evidence of the quality of ranking across languages, and open the final ranked lists for future research.
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