Abstract
Fuzzy string matching has many applications. Traditional approaches mainly use the appearance information of characters or words but do not use their semantic meanings. We postulate that the latter information may also be important for this task. To validate this hypothesis, we build a pipeline in which approximate string matching is used to pre-select some candidates and sentence embedding algorithms are used to select the final results from these candidates. The aim of sentence embedding is to represent semantic meaning of the words. Two sentence embedding algorithms are tested, convolutional neural network (CNN) and averaging word2vec. Experiments show that the proposed pipeline can significantly improve the accuracy and averaging word2vec works slightly better than CNN.
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Acknowledgements
This work was supported in part by the National Basic Research Program (973 Program) of China under Grant 2012CB316301 and Grant 2013CB329403, and in part by the National Natural Science Foundation of China under Grant 61273023, Grant 91420201, and Grant 61332007.
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Rong, Y., Hu, X. (2016). Fuzzy String Matching Using Sentence Embedding Algorithms. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_69
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DOI: https://doi.org/10.1007/978-3-319-46675-0_69
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