Ranking vs. Classification: A Case Study in Mining Organization Name Translation from Snippets

Abstract

Both classification and ranking strategy have been reported positively in mining the named entity (NE) translation from the snippets re-turned by the web search engine. Taking the most challenging issue of the organization name and its translation as an example, this paper conducts a contrastive study on the two strategies under SVM framework. We empirically show that the method of translation ranking achieves the best performance in various data settings, with the best Top-1 precision up to 65.75%. We conclude that, compared with the classification strategy, the ranking strategy is more suitable in such snippet based translation mining, in which the unbalance data issue prevails.