Chinese Paraphrases Acquiring Based on Random Walk N Steps

Jun Ma, Yujie Zhang, Jinan Xu, Yufeng Chen (Beijing Jiaotong University, Beijing, 100044, China ) Abstract: Conventional “pivot” approach of acquiring paraphrases from bilingual corpus has limitations, where only candidated paraphrases within two steps are considered. In this paper, we propose a graph based model of acquiring paraphrases from phrases translation table. First, we describe a graph model based on Chinese-English phrases translation table, a random walk algorithm based on N number of steps and a confidence metric for the obtained paraphrases phrases. Furthermore, we augment the model to be able to integrate more language pairs, for instance, English-Japanese phrases translation table aiming at 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 show that the proposed model acquired more paraphrases, and the performance was improved further after English-Japanese phrases translation was added into the graph model.