Abstract
Paraphrasing is a critical issue in many Natural Language Processing (NLP) applications. The traditional Pivot-based methods of extracting paraphrases require a large-scale bilingual parallel corpus. The quality of the extracted paraphrases is affected by the quality of bilingual parallel corpora and word alignment. In this paper, we propose a method for Chinese paraphrases extraction. An online translation system is used to obtain the candidate paraphrases of a word. A deep neural network model combined with cosine similarity is exploited to filter the candidate results through computing the similarity of word vectors between a word and its candidate paraphrase. Experiments are conducted in two ways: (1) The random sampling is employed to manually verify the correctness of the paraphrases results. The effect has been significantly improved; (2) We design two Question Answering (QA) systems based on the NLPCC2016 Document Based Question Answering (DBQA) corpus. One uses the BM25 model to retrieve the candidate answer sentences, and another uses the Convolution Neural Network (CNN) model. Extracted paraphrases are quite effective in question reformulation, enhancing the MRR from 56.33% to 60.21% (BM25) and from 63.82% to 66.60% (CNN) with the questions of NLPCC 2016 DBQA corpus.
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Acknowledgments
This work was supported by the National Basic Research Program (973 Program) (Grant No. 2014CB340503) and the National Natural Science Foundation of China (Grant No. 61472105 and 61502120).
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Zhang, Y., Qi, L., Wang, L., Yu, L., Liu, T. (2018). Beyond Pivot for Extracting Chinese Paraphrases. In: Zhang, S., Liu, TY., Li, X., Guo, J., Li, C. (eds) Information Retrieval. CCIR 2018. Lecture Notes in Computer Science(), vol 11168. Springer, Cham. https://doi.org/10.1007/978-3-030-01012-6_11
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