Chinese paraphrases acquisition based on random walk N step

Abstract

Conventional “pivot-based” approach of acquiring paraphrasing from bilingual corpus has limitations, where only paraphrases within two steps were considered. We propose a graph based model of acquiring paraphrases from phrases translation table. This paper describes the way of constructing graph model from phrases translation table, a random walk algorithm based on N number of steps and a confidence metric for ranking the obtained results. Furthermore, we augment the model to be able to integrate more language pairs, for instance, exploiting English-Japanese phrases translation table for finding more potential Chinese paraphrases. We performed experiments on NTCIR Chinese-English and English-Japanese bilingual corpora and compared with the conventional method. The experimental results showed that the proposed model acquired more paraphrases, and performed more well after English-Japanese phrases translation was added into the graph model.