Mining name translations from entity graph mapping

  • Authors:
  • Gae-won You;Seung-won Hwang;Young-In Song;Long Jiang;Zaiqing Nie

  • Affiliations:
  • Pohang University of Science and Technology, Pohang, Republic of Korea;Pohang University of Science and Technology, Pohang, Republic of Korea;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China

  • Venue:
  • EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
  • Year:
  • 2010

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Abstract

This paper studies the problem of mining entity translation, specifically, mining English and Chinese name pairs. Existing efforts can be categorized into (a) a transliteration-based approach leveraging phonetic similarity and (b) a corpus-based approach exploiting bilingual co-occurrences, each of which suffers from inaccuracy and scarcity respectively. In clear contrast, we use unleveraged resources of monolingual entity co-occurrences, crawled from entity search engines, represented as two entity-relationship graphs extracted from two language corpora respectively. Our problem is then abstracted as finding correct mappings across two graphs. To achieve this goal, we propose a holistic approach, of exploiting both transliteration similarity and monolingual co-occurrences. This approach, building upon monolingual corpora, complements existing corpus-based work, requiring scarce resources of parallel or comparable corpus, while significantly boosting the accuracy of transliteration-based work. We validate our proposed system using real-life datasets.