Reliable Classification of FAQs with Spelling Errors Using an Encoder-Decoder Neural Network in Korean
Program of Computer and Communications Engineering, Kangwon National University, Chuncheon 24341, Korea
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Appl. Sci. 2019, 9(22), 4758; https://doi.org/10.3390/app9224758
Received: 7 October 2019 / Revised: 30 October 2019 / Accepted: 3 November 2019 / Published: 7 November 2019
(This article belongs to the Special Issue Natural Language Processing: Emerging Neural Approaches and Applications)
To resolve lexical disagreement problems between queries and frequently asked questions (FAQs), we propose a reliable sentence classification model based on an encoder-decoder neural network. The proposed model uses three types of word embeddings; fixed word embeddings for representing domain-independent meanings of words, fined-tuned word embeddings for representing domain-specific meanings of words, and character-level word embeddings for bridging lexical gaps caused by spelling errors. It also uses class embeddings to represent domain knowledge associated with each category. In the experiments with an FAQ dataset about online banking, the proposed embedding methods contributed to an improved performance of the sentence classification. In addition, the proposed model showed better performance (with an accuracy of 0.810 in the classification of 411 categories) than that of the comparison model.
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MDPI and ACS Style
Jang, Y.; Kim, H. Reliable Classification of FAQs with Spelling Errors Using an Encoder-Decoder Neural Network in Korean. Appl. Sci. 2019, 9, 4758. https://doi.org/10.3390/app9224758
AMA Style
Jang Y, Kim H. Reliable Classification of FAQs with Spelling Errors Using an Encoder-Decoder Neural Network in Korean. Applied Sciences. 2019; 9(22):4758. https://doi.org/10.3390/app9224758
Chicago/Turabian StyleJang, Youngjin; Kim, Harksoo. 2019. "Reliable Classification of FAQs with Spelling Errors Using an Encoder-Decoder Neural Network in Korean" Appl. Sci. 9, no. 22: 4758. https://doi.org/10.3390/app9224758
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