Jamo Unit Convolutional Neural Network Based Automatic Classification of Frequently Asked Questions with Spelling Errors

Youngjin Jang, Harksoo Kim, Dongho Kang, Sebin Kim, Hyunki Jang

http://doi.org/10.5626/JOK.2019.46.6.563

Web and mobile users obtain the desired information using the frequently asked questions (FAQ) listed on the homepage. The FAQ system displays a query response candidate that is most similar to the input based on an information retrieval model. However, the information retrieval model depends on the index, and therefore, it is vulnerable to spelling errors in the sentence. This paper proposes a model applying the FAQ system to the sentence classifier, which minimizes the spelling errors. Using the embedded layer with jamo-based convolutional neural network, the spelling errors of the user input were reduced. The performance of the classifier was improved using class embedding and feed-forward neural network. As a result of 457 and 769 FAQ classifications, the Micro F1 score showed 81.32% p and 61.11% p performance, respectively. We used the sigmoid function to quantify the reliability of the model prediction.