A rule-based disambiguation method using networks and content features achieves F1 scores of 0.88 for Pinyin and 0.89 for character names on 80 annotated pairs from 65k physics papers, outperforming baselines via higher recall.
A chinese expert disambigua- tion method based on semi-supervised graph clustering.INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 6(2):197–204, APR 2015
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Bridging the Language Gap in Scholarly Data I: Enhancing Author Disambiguation Algorithms for Chinese Names
A rule-based disambiguation method using networks and content features achieves F1 scores of 0.88 for Pinyin and 0.89 for character names on 80 annotated pairs from 65k physics papers, outperforming baselines via higher recall.