2020
DOI: 10.1007/s11042-020-09148-2
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Neural spelling correction: translating incorrect sentences to correct sentences for multimedia

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Cited by 16 publications
(24 citation statements)
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“…Furthermore, the BLEU and GLEU scores of the Filter model were higher than those of the existing spelling correction model proposed by Park et al (2020a) by 5.94 and 15.15, respectively. These results show that our post-processor can achieve higher performance in spelling correction.…”
Section: Quantitative Resultsmentioning
confidence: 67%
See 2 more Smart Citations
“…Furthermore, the BLEU and GLEU scores of the Filter model were higher than those of the existing spelling correction model proposed by Park et al (2020a) by 5.94 and 15.15, respectively. These results show that our post-processor can achieve higher performance in spelling correction.…”
Section: Quantitative Resultsmentioning
confidence: 67%
“…Base refers to the BLEU score between the source and target sentences; we leveraged it as the baseline for assessing the performance improvement. In addition, we compared the performance with that of the Korean spelling error correction model proposed by Park et al (2020a), who performed ASR post-processing experiments and published the model as a demo system † . This study focused on Korean spelling error correction that is not specialized in ASR post-processing.…”
Section: Quantitative Resultsmentioning
confidence: 99%
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“…The S2S methodology corrects errors in the same way as the machine translation process Baskar et al, 2019;Park et al, 2020a). Based on the S2S model, the STT result is vectorized using an encoder and the vector is then decoded to generate a human-modified STT sentence.…”
Section: Sequence-to-sequence (S2s) Methodologymentioning
confidence: 99%
“…Therefore, this paper aims to develop an algorithm for name matching, that consider an approximate string-matching algorithm to allow dealing with possible technical or computational errors. Such matching algorithms have been used in several applications such as spelling correction (Park et al, 2020), linking databases (Hand & Christen, 2018), text retrieval (Abdulhayoglu, Thijs & Jeuris, 2016), handwriting recognition (Chowdhury, Bhattacharya & Parui, 2013), computational biology ''DNA'' (Berger, Waterman & Yu, 2020), and name recognition (Delgado et al, 2016), etc. Consequently, in this work, a new softened distance measure is proposed, based on the BI-DIST distance to increase the efficiency and accuracy of the name-matching method.…”
Section: Introductionmentioning
confidence: 99%