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.
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Acknowledgments
The research work has been partially funded by the International Science and Technology Cooperation Program of China under grant No. 2014DFA11350, National Nature Science Foundation of China (Contract 61370130 and 61473294), and the Fundamental Research Funds for the Central Universities (2015JBM033).
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Ma, J., Zhang, Y., Xu, J., Chen, Y. (2016). Chinese Paraphrases Acquisition Based on Random Walk N Step. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_57
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DOI: https://doi.org/10.1007/978-3-319-50496-4_57
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