Efficient Entity Translation Mining: A Parallelized Graph Alignment Approach

Abstract

This article studies the problem of mining entity translation, specifically, mining English and Chinese name pairs. Existing efforts can be categorized into (a) transliteration-based approaches that leverage phonetic similarity and (b) corpus-based approaches that exploit bilingual cooccurrences. These approaches suffer from inaccuracy and scarcity, respectively. In clear contrast, we use under-leveraged resources of monolingual entity cooccurrences crawled from entity search engines, which are 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 to exploiting both transliteration similarity and monolingual cooccurrences. This approach, which builds 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. In addition, by parallelizing the mapping process on multicore architectures, we speed up the computation by more than 10 times per unit accuracy. We validated the effectiveness and efficiency of our proposed approach using real-life datasets.