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2016 | OriginalPaper | Chapter

Fuzzy String Matching Using Sentence Embedding Algorithms

Authors : Yu Rong, Xiaolin Hu

Published in: Neural Information Processing

Publisher: Springer International Publishing

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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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Footnotes
Literature
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Metadata
Title
Fuzzy String Matching Using Sentence Embedding Algorithms
Authors
Yu Rong
Xiaolin Hu
Copyright Year
2016
Book
Neural Information Processing

Print ISBN: 978-3-319-46674-3

Electronic ISBN: 978-3-319-46675-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46675-0_69

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